Background

This analysis document compliments FIA NLS Models: Biomass Growth vs. Biomass. All of the background information from that document applies to these analyses, which are extensions to them. The difference between that document and this analysis is the use of different data subsets.

Here, we fit the models using: 1) a temporally-balanced dataset, where we take the first and most-recent plot record for all plots in the dataset, 2) a temporally-balanced dataset (same as #1), but which excludes plot locations which have experienced harvest (at any point over the study interval 2000-2022)

Below the model fitting procedure is implemented by ecoprovince:

Temporally-balancing the biomass growth data set

Lets look at some quick attributes of the dataset

  • The data set has 113960 observations, comprised of 57686 plots.
  • The frequency of growth measurements among plots is as follows (n=1 through 5): 25573, 13681, 12815, 5505, 112.
  • Thus 55.67% of plots have at least two growth measurements.

Analysis 1: Temporally-balanced analysis

211 - Northeastern Mixed Forest

model selection 1

## Analysis of Variance Table
## 
## Model 1: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
## Model 2: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * (1 - alpha * B_L_prop) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
##   Res.Df Res.Sum Sq Df Sum Sq F value    Pr(>F)    
## 1   4799     4403.7                                
## 2   4798     4218.7  1 185.03  210.43 < 2.2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##   model      AIC
## 1     1 18635.35
## 2     2 18431.23
## 
## Formula: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * (1 - 
##     alpha * B_L_prop) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
## 
## Parameters:
##       Estimate Std. Error t value Pr(>|t|)    
## tau    0.11645    0.16803   0.693    0.488    
## alpha  0.63059    0.04079  15.460   <2e-16 ***
## A      3.59795    0.12729  28.266   <2e-16 ***
## k      7.38570    0.78455   9.414   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.9377 on 4798 degrees of freedom
## 
## Number of iterations to convergence: 7 
## Achieved convergence tolerance: 3.026e-06
##   (36 observations deleted due to missingness)

summary

  • simple model: fits
  • alpha model: fits

model selection 2

## Error in nls(get(paste("fg_", Mod.Sel1, "a", sep = "")), data = G_211,  : 
##   object 'ge.fit' not found
## Error in nls(get(paste("fg_", Mod.Sel1, "b", sep = "")), data = G_211,  : 
##   object 'ge.fit' not found
## Error in nls(get(paste("fg_", Mod.Sel1, "c", sep = "")), data = G_211,  : 
##   object 'ge.fit' not found
##   model      AIC
## 1     2 18431.23
## 2    2a       NA
## 3    2b       NA
## 4    2c       NA
## 
## Formula: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * (1 - 
##     alpha * B_L_prop) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
## 
## Parameters:
##       Estimate Std. Error t value Pr(>|t|)    
## tau    0.11645    0.16803   0.693    0.488    
## alpha  0.63059    0.04079  15.460   <2e-16 ***
## A      3.59795    0.12729  28.266   <2e-16 ***
## k      7.38570    0.78455   9.414   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.9377 on 4798 degrees of freedom
## 
## Number of iterations to convergence: 7 
## Achieved convergence tolerance: 3.026e-06
##   (36 observations deleted due to missingness)

summary

  • add p model: does not fit
  • add s model: does not fit
  • add s+p model: does not fit

predict and plot

## Warning: Using `size` aesthetic for lines was deprecated in ggplot2 3.4.0.
## ℹ Please use `linewidth` instead.
## Warning: Removed 19 rows containing missing values (`geom_point()`).
## Warning: Removed 1050 rows containing missing values (`geom_line()`).

plotting 2

212 - Laurentian Mixed Forest

model selection 1

## Analysis of Variance Table
## 
## Model 1: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
## Model 2: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * (1 - alpha * B_L_prop) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
##   Res.Df Res.Sum Sq Df Sum Sq F value    Pr(>F)    
## 1   9768     9688.5                                
## 2   9767     9061.5  1 627.04  675.86 < 2.2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##   model      AIC
## 1     1 35804.93
## 2     2 35153.16
## 
## Formula: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * (1 - 
##     alpha * B_L_prop) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
## 
## Parameters:
##       Estimate Std. Error t value Pr(>|t|)    
## tau    1.23636    0.21162   5.842 5.31e-09 ***
## alpha  0.81205    0.02856  28.435  < 2e-16 ***
## A      2.54337    0.09180  27.706  < 2e-16 ***
## k     10.17903    0.59487  17.111  < 2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.9632 on 9767 degrees of freedom
## 
## Number of iterations to convergence: 7 
## Achieved convergence tolerance: 7.232e-06
##   (3191 observations deleted due to missingness)

summary

  • simple model: fits
  • alpha model: fits

model selection 2

## Error in nls(get(paste("fg_", Mod.Sel1, "a", sep = "")), data = G_212,  : 
##   object 'ge.fit' not found
## Error in nls(get(paste("fg_", Mod.Sel1, "b", sep = "")), data = G_212,  : 
##   object 'ge.fit' not found
## Error in nls(get(paste("fg_", Mod.Sel1, "c", sep = "")), data = G_212,  : 
##   object 'ge.fit' not found
##   model      AIC
## 1     2 35153.16
## 2    2a       NA
## 3    2b       NA
## 4    2c       NA
## 
## Formula: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * (1 - 
##     alpha * B_L_prop) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
## 
## Parameters:
##       Estimate Std. Error t value Pr(>|t|)    
## tau    1.23636    0.21162   5.842 5.31e-09 ***
## alpha  0.81205    0.02856  28.435  < 2e-16 ***
## A      2.54337    0.09180  27.706  < 2e-16 ***
## k     10.17903    0.59487  17.111  < 2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.9632 on 9767 degrees of freedom
## 
## Number of iterations to convergence: 7 
## Achieved convergence tolerance: 7.232e-06
##   (3191 observations deleted due to missingness)

summary

  • add p model: does not fit
  • add s model: does not fit
  • add s+p model: does not fit

predict and plot

## Warning: Removed 1591 rows containing missing values (`geom_point()`).
## Warning: Removed 1031 rows containing missing values (`geom_line()`).

plotting 2

221 - Eastern Broadleaf Forest

model selection 1

## Analysis of Variance Table
## 
## Model 1: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
## Model 2: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * (1 - alpha * B_L_prop) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
##   Res.Df Res.Sum Sq Df Sum Sq F value    Pr(>F)    
## 1   5407     7012.9                                
## 2   5406     6755.7  1 257.25  205.85 < 2.2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##   model      AIC
## 1     1 23593.91
## 2     2 23393.72
## 
## Formula: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * (1 - 
##     alpha * B_L_prop) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
## 
## Parameters:
##       Estimate Std. Error t value Pr(>|t|)    
## tau   -0.59298    0.14354  -4.131 3.66e-05 ***
## alpha  0.69426    0.04558  15.231  < 2e-16 ***
## A      4.89744    0.18018  27.181  < 2e-16 ***
## k     14.72783    1.83947   8.007 1.43e-15 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.118 on 5406 degrees of freedom
## 
## Number of iterations to convergence: 7 
## Achieved convergence tolerance: 3.879e-06
##   (36 observations deleted due to missingness)

summary

  • simple model: fits
  • alpha model: fits

model selection 2

## Error in nls(get(paste("fg_", Mod.Sel1, "a", sep = "")), data = G_221,  : 
##   object 'ge.fit' not found
## Error in nls(get(paste("fg_", Mod.Sel1, "b", sep = "")), data = G_221,  : 
##   object 'ge.fit' not found
## Error in nls(get(paste("fg_", Mod.Sel1, "c", sep = "")), data = G_221,  : 
##   object 'ge.fit' not found
##   model      AIC
## 1     2 23393.72
## 2    2a       NA
## 3    2b       NA
## 4    2c       NA
## 
## Formula: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * (1 - 
##     alpha * B_L_prop) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
## 
## Parameters:
##       Estimate Std. Error t value Pr(>|t|)    
## tau   -0.59298    0.14354  -4.131 3.66e-05 ***
## alpha  0.69426    0.04558  15.231  < 2e-16 ***
## A      4.89744    0.18018  27.181  < 2e-16 ***
## k     14.72783    1.83947   8.007 1.43e-15 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.118 on 5406 degrees of freedom
## 
## Number of iterations to convergence: 7 
## Achieved convergence tolerance: 3.879e-06
##   (36 observations deleted due to missingness)

summary

  • add p model: does not fit
  • add s model: does not fit
  • add s+p model: does not fit

predict and plot

## Warning: Removed 20 rows containing missing values (`geom_point()`).
## Warning: Removed 1036 rows containing missing values (`geom_line()`).

plotting 2

222 - Midwest Broadleaf Forest

model selection 1

## Analysis of Variance Table
## 
## Model 1: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
## Model 2: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * (1 - alpha * B_L_prop) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
##   Res.Df Res.Sum Sq Df Sum Sq F value    Pr(>F)    
## 1   2738     2904.7                                
## 2   2737     2699.1  1 205.61   208.5 < 2.2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##   model      AIC
## 1     1 10921.69
## 2     2 10722.46
## 
## Formula: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * (1 - 
##     alpha * B_L_prop) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
## 
## Parameters:
##       Estimate Std. Error t value Pr(>|t|)    
## tau    0.20704    0.27193   0.761    0.446    
## alpha  0.84073    0.05282  15.917   <2e-16 ***
## A      4.20770    0.24329  17.295   <2e-16 ***
## k     20.43092    2.04843   9.974   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.993 on 2737 degrees of freedom
## 
## Number of iterations to convergence: 9 
## Achieved convergence tolerance: 3.614e-06
##   (811 observations deleted due to missingness)

summary

  • simple model: fits
  • alpha model: fits

model selection 2

## Error in nls(get(paste("fg_", Mod.Sel1, "a", sep = "")), data = G_222,  : 
##   object 'ge.fit' not found
## Error in nls(get(paste("fg_", Mod.Sel1, "b", sep = "")), data = G_222,  : 
##   object 'ge.fit' not found
## Error in nls(get(paste("fg_", Mod.Sel1, "c", sep = "")), data = G_222,  : 
##   object 'ge.fit' not found
##   model      AIC
## 1     2 10722.46
## 2    2a       NA
## 3    2b       NA
## 4    2c       NA
## 
## Formula: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * (1 - 
##     alpha * B_L_prop) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
## 
## Parameters:
##       Estimate Std. Error t value Pr(>|t|)    
## tau    0.20704    0.27193   0.761    0.446    
## alpha  0.84073    0.05282  15.917   <2e-16 ***
## A      4.20770    0.24329  17.295   <2e-16 ***
## k     20.43092    2.04843   9.974   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.993 on 2737 degrees of freedom
## 
## Number of iterations to convergence: 9 
## Achieved convergence tolerance: 3.614e-06
##   (811 observations deleted due to missingness)

summary

  • add p model: does not fit
  • add s model: does not fit
  • add s+p model: does not fit

predict and plot

## Warning: Removed 419 rows containing missing values (`geom_point()`).
## Warning: Removed 1108 rows containing missing values (`geom_line()`).

plotting 2

223 - Central Interior Broadleaf Forest

model selection 1

## Analysis of Variance Table
## 
## Model 1: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
## Model 2: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * (1 - alpha * B_L_prop) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
##   Res.Df Res.Sum Sq Df Sum Sq F value    Pr(>F)    
## 1   5257     6464.4                                
## 2   5256     6291.4  1 172.96  144.49 < 2.2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##   model      AIC
## 1     1 21832.82
## 2     2 21692.17
## 
## Formula: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * (1 - 
##     alpha * B_L_prop) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
## 
## Parameters:
##       Estimate Std. Error t value Pr(>|t|)    
## tau   -0.83553    0.12268  -6.811 1.08e-11 ***
## alpha  0.64175    0.05008  12.815  < 2e-16 ***
## A      5.20077    0.20318  25.596  < 2e-16 ***
## k     30.29086    3.15408   9.604  < 2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.094 on 5256 degrees of freedom
## 
## Number of iterations to convergence: 12 
## Achieved convergence tolerance: 9.644e-06
##   (1128 observations deleted due to missingness)

summary

  • simple model: fits
  • alpha model: fits

model selection 2

## Error in nls(get(paste("fg_", Mod.Sel1, "a", sep = "")), data = G_223,  : 
##   object 'ge.fit' not found
## Error in nls(get(paste("fg_", Mod.Sel1, "b", sep = "")), data = G_223,  : 
##   object 'ge.fit' not found
## Error in nls(get(paste("fg_", Mod.Sel1, "c", sep = "")), data = G_223,  : 
##   object 'ge.fit' not found
##   model      AIC
## 1     2 21692.17
## 2    2a       NA
## 3    2b       NA
## 4    2c       NA
## 
## Formula: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * (1 - 
##     alpha * B_L_prop) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
## 
## Parameters:
##       Estimate Std. Error t value Pr(>|t|)    
## tau   -0.83553    0.12268  -6.811 1.08e-11 ***
## alpha  0.64175    0.05008  12.815  < 2e-16 ***
## A      5.20077    0.20318  25.596  < 2e-16 ***
## k     30.29086    3.15408   9.604  < 2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.094 on 5256 degrees of freedom
## 
## Number of iterations to convergence: 12 
## Achieved convergence tolerance: 9.644e-06
##   (1128 observations deleted due to missingness)

summary

  • add p model: does not fit
  • add s model: does not fit
  • add s+p model: does not fit

predict and plot

## Warning: Removed 579 rows containing missing values (`geom_point()`).
## Warning: Removed 1145 rows containing missing values (`geom_line()`).

plotting 2

231 - Southeastern Mixed Forest

model selection 1

## Analysis of Variance Table
## 
## Model 1: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
## Model 2: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * (1 - alpha * B_L_prop) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
##   Res.Df Res.Sum Sq Df Sum Sq F value    Pr(>F)    
## 1   7654      15414                                
## 2   7653      13881  1 1532.7  845.01 < 2.2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##   model      AIC
## 1     1 38274.53
## 2     2 37474.57
## 
## Formula: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * (1 - 
##     alpha * B_L_prop) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
## 
## Parameters:
##       Estimate Std. Error t value Pr(>|t|)    
## tau    1.56725    0.21078   7.436 1.15e-13 ***
## alpha  0.87196    0.02725  31.999  < 2e-16 ***
## A      3.83483    0.12639  30.340  < 2e-16 ***
## k      1.35721    0.22062   6.152 8.05e-10 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.347 on 7653 degrees of freedom
## 
## Number of iterations to convergence: 10 
## Achieved convergence tolerance: 7.675e-06
##   (133 observations deleted due to missingness)

summary

  • simple model: fits
  • alpha model: fits

model selection 2

## Error in nls(get(paste("fg_", Mod.Sel1, "a", sep = "")), data = G_231,  : 
##   object 'ge.fit' not found
## Error in nls(get(paste("fg_", Mod.Sel1, "b", sep = "")), data = G_231,  : 
##   object 'ge.fit' not found
## Error in nls(get(paste("fg_", Mod.Sel1, "c", sep = "")), data = G_231,  : 
##   object 'ge.fit' not found
##   model      AIC
## 1     2 37474.57
## 2    2a       NA
## 3    2b       NA
## 4    2c       NA
## 
## Formula: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * (1 - 
##     alpha * B_L_prop) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
## 
## Parameters:
##       Estimate Std. Error t value Pr(>|t|)    
## tau    1.56725    0.21078   7.436 1.15e-13 ***
## alpha  0.87196    0.02725  31.999  < 2e-16 ***
## A      3.83483    0.12639  30.340  < 2e-16 ***
## k      1.35721    0.22062   6.152 8.05e-10 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.347 on 7653 degrees of freedom
## 
## Number of iterations to convergence: 10 
## Achieved convergence tolerance: 7.675e-06
##   (133 observations deleted due to missingness)

summary

  • add p model: does not fit
  • add s model: does not fit
  • add s+p model: does not fit

predict and plot

## Warning: Removed 67 rows containing missing values (`geom_point()`).
## Warning: Removed 1017 rows containing missing values (`geom_line()`).

plotting 2

232 - Outer Coastal Plain Mixed Forest

model selection 1

## Analysis of Variance Table
## 
## Model 1: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
## Model 2: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * (1 - alpha * B_L_prop) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
##   Res.Df Res.Sum Sq Df Sum Sq F value    Pr(>F)    
## 1   7765      19227                                
## 2   7764      17588  1 1639.3  723.69 < 2.2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##   model      AIC
## 1     1 39432.03
## 2     2 38741.76
## 
## Formula: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * (1 - 
##     alpha * B_L_prop) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
## 
## Parameters:
##       Estimate Std. Error t value Pr(>|t|)    
## tau    1.12531    0.21903   5.138 2.85e-07 ***
## alpha  0.86821    0.02892  30.018  < 2e-16 ***
## A      4.01181    0.15286  26.245  < 2e-16 ***
## k      5.60838    0.54424  10.305  < 2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.505 on 7764 degrees of freedom
## 
## Number of iterations to convergence: 7 
## Achieved convergence tolerance: 9.58e-06
##   (172 observations deleted due to missingness)

summary

  • simple model: fits
  • alpha model: fits

model selection 2

## Error in nls(get(paste("fg_", Mod.Sel1, "a", sep = "")), data = G_232,  : 
##   object 'ge.fit' not found
## Error in nls(get(paste("fg_", Mod.Sel1, "b", sep = "")), data = G_232,  : 
##   object 'ge.fit' not found
## Error in nls(get(paste("fg_", Mod.Sel1, "c", sep = "")), data = G_232,  : 
##   object 'ge.fit' not found
##   model      AIC
## 1     2 38741.76
## 2    2a       NA
## 3    2b       NA
## 4    2c       NA
## 
## Formula: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * (1 - 
##     alpha * B_L_prop) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
## 
## Parameters:
##       Estimate Std. Error t value Pr(>|t|)    
## tau    1.12531    0.21903   5.138 2.85e-07 ***
## alpha  0.86821    0.02892  30.018  < 2e-16 ***
## A      4.01181    0.15286  26.245  < 2e-16 ***
## k      5.60838    0.54424  10.305  < 2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.505 on 7764 degrees of freedom
## 
## Number of iterations to convergence: 7 
## Achieved convergence tolerance: 9.58e-06
##   (172 observations deleted due to missingness)

summary

  • add p model: does not fit
  • add s model: does not fit
  • add s+p model: does not fit

predict and plot

## Warning: Removed 91 rows containing missing values (`geom_point()`).
## Warning: Removed 981 rows containing missing values (`geom_line()`).

plotting 2

234 - Lower Mississippi Riverine Forest

model selection 1

## Analysis of Variance Table
## 
## Model 1: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
## Model 2: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * (1 - alpha * B_L_prop) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
##   Res.Df Res.Sum Sq Df Sum Sq F value    Pr(>F)    
## 1    798     1787.7                                
## 2    797     1679.2  1 108.44  51.471 1.663e-12 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##   model      AIC
## 1     1 4024.928
## 2     2 3976.801
## 
## Formula: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * (1 - 
##     alpha * B_L_prop) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
## 
## Parameters:
##       Estimate Std. Error t value Pr(>|t|)    
## tau    0.53177    0.68863   0.772    0.440    
## alpha  0.78767    0.09903   7.954 6.18e-15 ***
## A      4.11911    0.55948   7.362 4.51e-13 ***
## k      2.01994    1.23877   1.631    0.103    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.452 on 797 degrees of freedom
## 
## Number of iterations to convergence: 7 
## Achieved convergence tolerance: 5.905e-06
##   (29 observations deleted due to missingness)

summary

  • simple model: fits
  • alpha model: fits

model selection 2

## Error in nls(get(paste("fg_", Mod.Sel1, "a", sep = "")), data = G_234,  : 
##   object 'ge.fit' not found
## Error in nls(get(paste("fg_", Mod.Sel1, "b", sep = "")), data = G_234,  : 
##   object 'ge.fit' not found
## Error in nls(get(paste("fg_", Mod.Sel1, "c", sep = "")), data = G_234,  : 
##   object 'ge.fit' not found
##   model      AIC
## 1     2 3976.801
## 2    2a       NA
## 3    2b       NA
## 4    2c       NA
## 
## Formula: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * (1 - 
##     alpha * B_L_prop) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
## 
## Parameters:
##       Estimate Std. Error t value Pr(>|t|)    
## tau    0.53177    0.68863   0.772    0.440    
## alpha  0.78767    0.09903   7.954 6.18e-15 ***
## A      4.11911    0.55948   7.362 4.51e-13 ***
## k      2.01994    1.23877   1.631    0.103    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.452 on 797 degrees of freedom
## 
## Number of iterations to convergence: 7 
## Achieved convergence tolerance: 5.905e-06
##   (29 observations deleted due to missingness)

summary

  • add p model: does not fit
  • add s model: does not fit
  • add s+p model: does not fit

predict and plot

## Warning: Removed 10 rows containing missing values (`geom_point()`).
## Warning: Removed 1077 rows containing missing values (`geom_line()`).

plotting 2

242 - Pacific Lowland Mixed Forest

model selection 1

## Error in if (any(nEQ <- vNms != make.names(vNms))) vNms[nEQ] <- paste0("`",  : 
##   missing value where TRUE/FALSE needed
## Error in if (any(nEQ <- vNms != make.names(vNms))) vNms[nEQ] <- paste0("`",  : 
##   missing value where TRUE/FALSE needed
##   model AIC
## 1     1  NA
## 2     2  NA
## Warning in min(AIC1_242$AIC, na.rm = T): no non-missing arguments to min;
## returning Inf
## Error in h(simpleError(msg, call)) : 
##   error in evaluating the argument 'object' in selecting a method for function 'summary': object 'nls_242.' not found

summary

  • simple model: does not fit
  • alpha model: does not fit

model selection 2

summary

  • add p model: does not fit
  • add s model: does not fit
  • add s+p model: does not fit

predict and plot

## [1] "cannot plot data with prediction"

plotting 2

251 - Prairie Parkland (Temperate)

model selection 1

## Analysis of Variance Table
## 
## Model 1: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
## Model 2: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * (1 - alpha * B_L_prop) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
##   Res.Df Res.Sum Sq Df Sum Sq F value   Pr(>F)   
## 1    977     1385.2                              
## 2    976     1373.9  1 11.286  8.0178 0.004727 **
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##   model      AIC
## 1     1 4120.597
## 2     2 4114.579
## 
## Formula: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * (1 - 
##     alpha * B_L_prop) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
## 
## Parameters:
##       Estimate Std. Error t value Pr(>|t|)    
## tau    0.01826    0.53851   0.034  0.97296    
## alpha  0.45506    0.15286   2.977  0.00298 ** 
## A      3.31919    0.39912   8.316 3.03e-16 ***
## k     10.40557    3.41241   3.049  0.00236 ** 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.186 on 976 degrees of freedom
## 
## Number of iterations to convergence: 12 
## Achieved convergence tolerance: 6.762e-06
##   (412 observations deleted due to missingness)

summary

  • simple model: fits
  • alpha model: fits

model selection 2

## Error in nls(get(paste("fg_", Mod.Sel1, "a", sep = "")), data = G_251,  : 
##   object 'ge.fit' not found
## Error in nls(get(paste("fg_", Mod.Sel1, "b", sep = "")), data = G_251,  : 
##   object 'ge.fit' not found
## Error in nls(get(paste("fg_", Mod.Sel1, "c", sep = "")), data = G_251,  : 
##   object 'ge.fit' not found
##   model      AIC
## 1     2 4114.579
## 2    2a       NA
## 3    2b       NA
## 4    2c       NA
## 
## Formula: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * (1 - 
##     alpha * B_L_prop) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
## 
## Parameters:
##       Estimate Std. Error t value Pr(>|t|)    
## tau    0.01826    0.53851   0.034  0.97296    
## alpha  0.45506    0.15286   2.977  0.00298 ** 
## A      3.31919    0.39912   8.316 3.03e-16 ***
## k     10.40557    3.41241   3.049  0.00236 ** 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.186 on 976 degrees of freedom
## 
## Number of iterations to convergence: 12 
## Achieved convergence tolerance: 6.762e-06
##   (412 observations deleted due to missingness)

summary

  • add p model: does not fit
  • add s model: does not fit
  • add s+p model: does not fit

predict and plot

## Warning: Removed 224 rows containing missing values (`geom_point()`).
## Warning: Removed 1176 rows containing missing values (`geom_line()`).

plotting 2

255 - Prairie Parkland (Subtropical)

model selection 1

## Error in nls(fg_1, data = G_255, start = c(tau = tau.start, A = A.start,  : 
##   number of iterations exceeded maximum of 50
## Error in nls(fg_2, data = G_255, start = c(tau = tau.start, alpha = alpha.start,  : 
##   number of iterations exceeded maximum of 50
##   model AIC
## 1     1  NA
## 2     2  NA
## Warning in min(AIC1_255$AIC, na.rm = T): no non-missing arguments to min;
## returning Inf
## Error in h(simpleError(msg, call)) : 
##   error in evaluating the argument 'object' in selecting a method for function 'summary': object 'nls_255.' not found

summary

  • simple model: does not fit
  • alpha model: does not fit

model selection 2

summary

  • add p model: does not fit
  • add s model: does not fit
  • add s+p model: does not fit

predict and plot

## [1] "cannot plot data with prediction"

plotting 2

261 - California Coastal Chaparral Forest and Shrub

model selection 1

summary

  • simple model: does not fit
  • alpha model: does not fit

model selection 2

summary

  • add p model: does not fit
  • add s model: does not fit
  • add s+p model: does not fit

predict and plot

## [1] "cannot plot data with prediction"

plotting 2

262 - California Dry Steppe

model selection 1

summary

  • simple model: does not fit
  • alpha model: does not fit

model selection 2

summary

  • add p model: does not fit
  • add s model: does not fit
  • add s+p model: does not fit

predict and plot

## [1] "cannot plot data with prediction"

plotting 2

263 - California Coastal Steppe - Mixed Forest and Redwood Forest

model selection 1

summary

  • simple model: does not fit
  • alpha model: does not fit

model selection 2

summary

  • add p model: does not fit

  • add s model: does not fit

  • add s+p model: does not fit

  • note: model fit, but fit was funky due to data being sparse

predict and plot

## [1] "cannot plot data with prediction"

plotting 2

313 - Colorado Plateau Semi-Desert

model selection 1

## Error in if (any(nEQ <- vNms != make.names(vNms))) vNms[nEQ] <- paste0("`",  : 
##   missing value where TRUE/FALSE needed
## Error in if (any(nEQ <- vNms != make.names(vNms))) vNms[nEQ] <- paste0("`",  : 
##   missing value where TRUE/FALSE needed
##   model AIC
## 1     1  NA
## 2     2  NA
## Warning in min(AIC1_313$AIC, na.rm = T): no non-missing arguments to min;
## returning Inf
## Error in h(simpleError(msg, call)) : 
##   error in evaluating the argument 'object' in selecting a method for function 'summary': object 'nls_313.' not found

summary

  • simple model: does not fit
  • alpha model: does not fit

model selection 2

summary

  • add p model: does not fit
  • add s model: does not fit
  • add s+p model: does not fit

predict and plot

## [1] "cannot plot data with prediction"

plotting 2

315 - Southwest Plateau and Plains Dry Steppe and Shrub

model selection 1

summary

  • simple model: does not fit
  • alpha model: does not fit

model selection 2

summary

  • add p model: does not fit
  • add s model: does not fit
  • add s+p model: does not fit

predict and plot

## [1] "cannot plot data with prediction"

plotting 2

321 - Chihuahuan Semi-Desert

model selection 1

summary

  • simple model: does not fit
  • alpha model: does not fit

model selection 2

summary

  • add p model: does not fit
  • add s model: does not fit
  • add s+p model: does not fit

predict and plot

## [1] "cannot plot data with prediction"

plotting 2

322 - American Semidesert and Desert

model selection 1

summary

  • simple model: does not fit
  • alpha model: does not fit

model selection 2

summary

  • add p model: does not fit
  • add s model: does not fit
  • add s+p model: does not fit

predict and plot

## [1] "cannot plot data with prediction"

plotting 2

331 - Great Plains/Palouse Dry Steppe

model selection 1

## Error in nls(fg_1, data = G_331, start = c(tau = tau.start, A = A.start,  : 
##   number of iterations exceeded maximum of 50
## Error in nls(fg_2, data = G_331, start = c(tau = tau.start, alpha = alpha.start,  : 
##   number of iterations exceeded maximum of 50
##   model AIC
## 1     1  NA
## 2     2  NA
## Warning in min(AIC1_331$AIC, na.rm = T): no non-missing arguments to min;
## returning Inf
## Error in h(simpleError(msg, call)) : 
##   error in evaluating the argument 'object' in selecting a method for function 'summary': object 'nls_331.' not found

summary

  • simple model: does not fit
  • alpha model: does not fit

model selection 2

summary

  • add p model: does not fit
  • add s model: does not fit
  • add s+p model: does not fit

predict and plot

## [1] "cannot plot data with prediction"

plotting 2

332 - Great Plains Steppe

model selection 1

## Analysis of Variance Table
## 
## Model 1: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
## Model 2: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * (1 - alpha * B_L_prop) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
##   Res.Df Res.Sum Sq Df Sum Sq F value  Pr(>F)  
## 1    136     120.26                            
## 2    135     117.52  1 2.7361   3.143 0.07851 .
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##   model      AIC
## 1     1 459.1608
## 2     2 457.9617
## 
## Formula: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * (1 - 
##     alpha * B_L_prop) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
## 
## Parameters:
##       Estimate Std. Error t value Pr(>|t|)  
## tau     1.5868     2.5256   0.628   0.5309  
## alpha   0.5938     0.3038   1.955   0.0527 .
## A       2.9848     1.3104   2.278   0.0243 *
## k      58.3210    24.2463   2.405   0.0175 *
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.933 on 135 degrees of freedom
## 
## Number of iterations to convergence: 6 
## Achieved convergence tolerance: 1.529e-06
##   (15 observations deleted due to missingness)

summary

  • simple model: fits
  • alpha model: fits

model selection 2

## Error in nls(get(paste("fg_", Mod.Sel1, "a", sep = "")), data = G_332,  : 
##   object 'ge.fit' not found
## Error in nls(get(paste("fg_", Mod.Sel1, "b", sep = "")), data = G_332,  : 
##   object 'ge.fit' not found
## Error in nls(get(paste("fg_", Mod.Sel1, "c", sep = "")), data = G_332,  : 
##   object 'ge.fit' not found
##   model      AIC
## 1     2 457.9617
## 2    2a       NA
## 3    2b       NA
## 4    2c       NA
## 
## Formula: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * (1 - 
##     alpha * B_L_prop) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
## 
## Parameters:
##       Estimate Std. Error t value Pr(>|t|)  
## tau     1.5868     2.5256   0.628   0.5309  
## alpha   0.5938     0.3038   1.955   0.0527 .
## A       2.9848     1.3104   2.278   0.0243 *
## k      58.3210    24.2463   2.405   0.0175 *
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.933 on 135 degrees of freedom
## 
## Number of iterations to convergence: 6 
## Achieved convergence tolerance: 1.529e-06
##   (15 observations deleted due to missingness)

summary

  • add p model: does not fit
  • add s model: does not fit
  • add s+p model: does not fit

predict and plot

## Warning: Removed 7 rows containing missing values (`geom_point()`).
## Warning: Removed 1140 rows containing missing values (`geom_line()`).

plotting 2

341 - Intermountain Semi-desert & Desert

model selection 1

summary

  • simple model: does not fit
  • alpha model: does not fit

model selection 2

summary

  • add p model: does not fit
  • add s model: does not fit
  • add s+p model: does not fit

predict and plot

## [1] "cannot plot data with prediction"

plotting 2

342 - Intermountain Semi-Desert

model selection 1

## Error in nls(fg_1, data = G_342, start = c(tau = tau.start, A = A.start,  : 
##   number of iterations exceeded maximum of 50
## Error in nls(fg_2, data = G_342, start = c(tau = tau.start, alpha = alpha.start,  : 
##   number of iterations exceeded maximum of 50
##   model AIC
## 1     1  NA
## 2     2  NA
## Warning in min(AIC1_342$AIC, na.rm = T): no non-missing arguments to min;
## returning Inf
## Error in h(simpleError(msg, call)) : 
##   error in evaluating the argument 'object' in selecting a method for function 'summary': object 'nls_342.' not found

summary

  • simple model: does not fit
  • alpha model: does not fit

model selection 2

summary

  • add p model: does not fit
  • add s model: does not fit
  • add s+p model: does not fit

predict and plot

## [1] "cannot plot data with prediction"

plotting 2

411 - Everglades

model selection 1

summary

  • simple model: does not fit
  • alpha model: does not fit

model selection 2

summary

  • add p model: does not fit
  • add s model: does not fit
  • add s+p model: does not fit

predict and plot

## [1] "cannot plot data with prediction"

plotting 2

M211 - Adirondack-New England Mixed forest - Coniferous Forest - Alpine Meadow

model selection 1

## Analysis of Variance Table
## 
## Model 1: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
## Model 2: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * (1 - alpha * B_L_prop) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
##   Res.Df Res.Sum Sq Df Sum Sq F value    Pr(>F)    
## 1   5091     4294.6                                
## 2   5090     4058.8  1 235.79  295.69 < 2.2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##   model      AIC
## 1     1 19299.92
## 2     2 19014.27
## 
## Formula: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * (1 - 
##     alpha * B_L_prop) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
## 
## Parameters:
##       Estimate Std. Error t value Pr(>|t|)    
## tau    0.90463    0.22963   3.939 8.28e-05 ***
## alpha  0.64109    0.03478  18.435  < 2e-16 ***
## A      2.91302    0.12155  23.965  < 2e-16 ***
## k      2.85528    0.48360   5.904 3.77e-09 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.893 on 5090 degrees of freedom
## 
## Number of iterations to convergence: 7 
## Achieved convergence tolerance: 3.974e-06
##   (14 observations deleted due to missingness)

summary

  • simple model: fits
  • alpha model: fits

model selection 2

## Error in nls(get(paste("fg_", Mod.Sel1, "a", sep = "")), data = G_M211,  : 
##   object 'ge.fit' not found
## Error in nls(get(paste("fg_", Mod.Sel1, "b", sep = "")), data = G_M211,  : 
##   object 'ge.fit' not found
## Error in nls(get(paste("fg_", Mod.Sel1, "c", sep = "")), data = G_M211,  : 
##   object 'ge.fit' not found
##   model      AIC
## 1     2 19014.27
## 2    2a       NA
## 3    2b       NA
## 4    2c       NA
## 
## Formula: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * (1 - 
##     alpha * B_L_prop) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
## 
## Parameters:
##       Estimate Std. Error t value Pr(>|t|)    
## tau    0.90463    0.22963   3.939 8.28e-05 ***
## alpha  0.64109    0.03478  18.435  < 2e-16 ***
## A      2.91302    0.12155  23.965  < 2e-16 ***
## k      2.85528    0.48360   5.904 3.77e-09 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.893 on 5090 degrees of freedom
## 
## Number of iterations to convergence: 7 
## Achieved convergence tolerance: 3.974e-06
##   (14 observations deleted due to missingness)

summary

  • add p model: does not fit
  • add s model: does not fit
  • add s+p model: does not fit

predict and plot

## Warning: Removed 6 rows containing missing values (`geom_point()`).
## Warning: Removed 1108 rows containing missing values (`geom_line()`).

plotting 2

M221 - Eastern Broadleaf Forest

model selection 1

## Analysis of Variance Table
## 
## Model 1: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
## Model 2: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * (1 - alpha * B_L_prop) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
##   Res.Df Res.Sum Sq Df Sum Sq F value    Pr(>F)    
## 1   5156     9837.2                                
## 2   5155     9614.7  1 222.49  119.29 < 2.2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##   model      AIC
## 1     1 24619.42
## 2     2 24503.40
## 
## Formula: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * (1 - 
##     alpha * B_L_prop) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
## 
## Parameters:
##       Estimate Std. Error t value Pr(>|t|)    
## tau    0.45747    0.22378   2.044    0.041 *  
## alpha  0.80043    0.06929  11.552  < 2e-16 ***
## A      3.80503    0.17110  22.238  < 2e-16 ***
## k      8.31974    1.87264   4.443 9.07e-06 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.366 on 5155 degrees of freedom
## 
## Number of iterations to convergence: 7 
## Achieved convergence tolerance: 8.2e-06
##   (27 observations deleted due to missingness)

summary

  • simple model: fits
  • alpha model: fits

model selection 2

## Error in nls(get(paste("fg_", Mod.Sel1, "a", sep = "")), data = G_M221,  : 
##   object 'ge.fit' not found
## Error in nls(get(paste("fg_", Mod.Sel1, "b", sep = "")), data = G_M221,  : 
##   object 'ge.fit' not found
## Error in nls(get(paste("fg_", Mod.Sel1, "c", sep = "")), data = G_M221,  : 
##   object 'ge.fit' not found
##   model     AIC
## 1     2 24503.4
## 2    2a      NA
## 3    2b      NA
## 4    2c      NA
## 
## Formula: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * (1 - 
##     alpha * B_L_prop) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
## 
## Parameters:
##       Estimate Std. Error t value Pr(>|t|)    
## tau    0.45747    0.22378   2.044    0.041 *  
## alpha  0.80043    0.06929  11.552  < 2e-16 ***
## A      3.80503    0.17110  22.238  < 2e-16 ***
## k      8.31974    1.87264   4.443 9.07e-06 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.366 on 5155 degrees of freedom
## 
## Number of iterations to convergence: 7 
## Achieved convergence tolerance: 8.2e-06
##   (27 observations deleted due to missingness)

summary

  • add p model: does not fit
  • add s model: does not fit
  • add s+p model: does not fit

predict and plot

## Warning: Removed 15 rows containing missing values (`geom_point()`).
## Warning: Removed 982 rows containing missing values (`geom_line()`).

plotting 2

M223 - Ozark Broadleaf Forest Meadow

model selection 1

## Analysis of Variance Table
## 
## Model 1: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
## Model 2: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * (1 - alpha * B_L_prop) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
##   Res.Df Res.Sum Sq Df Sum Sq F value    Pr(>F)    
## 1    596     960.49                                
## 2    595     931.66  1 28.823  18.408 2.081e-05 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##   model      AIC
## 1     1 2564.160
## 2     2 2547.909
## 
## Formula: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * (1 - 
##     alpha * B_L_prop) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
## 
## Parameters:
##       Estimate Std. Error t value Pr(>|t|)    
## tau     3.0417     1.6720   1.819   0.0694 .  
## alpha   0.9604     0.2071   4.638 4.32e-06 ***
## A       1.9026     0.4356   4.368 1.48e-05 ***
## k       9.2719     6.2331   1.488   0.1374    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.251 on 595 degrees of freedom
## 
## Number of iterations to convergence: 12 
## Achieved convergence tolerance: 6.676e-06
##   (3 observations deleted due to missingness)

summary

  • simple model: fits
  • alpha model: fits

model selection 2

## Error in nls(get(paste("fg_", Mod.Sel1, "a", sep = "")), data = G_M223,  : 
##   object 'ge.fit' not found
## Error in nls(get(paste("fg_", Mod.Sel1, "b", sep = "")), data = G_M223,  : 
##   object 'ge.fit' not found
## Error in nls(get(paste("fg_", Mod.Sel1, "c", sep = "")), data = G_M223,  : 
##   object 'ge.fit' not found
##   model      AIC
## 1     2 2547.909
## 2    2a       NA
## 3    2b       NA
## 4    2c       NA
## 
## Formula: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * (1 - 
##     alpha * B_L_prop) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
## 
## Parameters:
##       Estimate Std. Error t value Pr(>|t|)    
## tau     3.0417     1.6720   1.819   0.0694 .  
## alpha   0.9604     0.2071   4.638 4.32e-06 ***
## A       1.9026     0.4356   4.368 1.48e-05 ***
## k       9.2719     6.2331   1.488   0.1374    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.251 on 595 degrees of freedom
## 
## Number of iterations to convergence: 12 
## Achieved convergence tolerance: 6.676e-06
##   (3 observations deleted due to missingness)

summary

  • add p model: does not fit
  • add s model: does not fit
  • add s+p model: does not fit

predict and plot

## Warning: Removed 2 rows containing missing values (`geom_point()`).
## Warning: Removed 1175 rows containing missing values (`geom_line()`).

plotting 2

M231 - Ouachita Mixed Forest

model selection 1

## Analysis of Variance Table
## 
## Model 1: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
## Model 2: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * (1 - alpha * B_L_prop) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
##   Res.Df Res.Sum Sq Df Sum Sq F value    Pr(>F)    
## 1    670     950.61                                
## 2    669     910.08  1 40.524  29.789 6.792e-08 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##   model      AIC
## 1     1 2806.978
## 2     2 2779.659
## 
## Formula: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * (1 - 
##     alpha * B_L_prop) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
## 
## Parameters:
##       Estimate Std. Error t value Pr(>|t|)    
## tau     7.7044     4.5986   1.675  0.09433 .  
## alpha   0.9156     0.1561   5.864  7.1e-09 ***
## A       1.1221     0.4247   2.642  0.00842 ** 
## k       3.4481     2.0307   1.698  0.08997 .  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.166 on 669 degrees of freedom
## 
## Number of iterations to convergence: 16 
## Achieved convergence tolerance: 7.883e-06
##   (7 observations deleted due to missingness)

summary

  • simple model: fits
  • alpha model: fits

model selection 2

## Error in nls(get(paste("fg_", Mod.Sel1, "a", sep = "")), data = G_M231,  : 
##   object 'ge.fit' not found
## Error in nls(get(paste("fg_", Mod.Sel1, "b", sep = "")), data = G_M231,  : 
##   object 'ge.fit' not found
## Error in nls(get(paste("fg_", Mod.Sel1, "c", sep = "")), data = G_M231,  : 
##   object 'ge.fit' not found
##   model      AIC
## 1     2 2779.659
## 2    2a       NA
## 3    2b       NA
## 4    2c       NA
## 
## Formula: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * (1 - 
##     alpha * B_L_prop) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
## 
## Parameters:
##       Estimate Std. Error t value Pr(>|t|)    
## tau     7.7044     4.5986   1.675  0.09433 .  
## alpha   0.9156     0.1561   5.864  7.1e-09 ***
## A       1.1221     0.4247   2.642  0.00842 ** 
## k       3.4481     2.0307   1.698  0.08997 .  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.166 on 669 degrees of freedom
## 
## Number of iterations to convergence: 16 
## Achieved convergence tolerance: 7.883e-06
##   (7 observations deleted due to missingness)

summary

  • add p model: does not fit
  • add s model: does not fit
  • add s+p model: does not fit

predict and plot

## Warning: Removed 6 rows containing missing values (`geom_point()`).
## Warning: Removed 1218 rows containing missing values (`geom_line()`).

plotting 2

M242 - Cascade Mixed Forest

model selection 1

summary

  • simple model: does not fit
  • alpha model: does not fit

model selection 2

summary

  • add p model: does not fit
  • add s model: does not fit
  • add s+p model: does not fit

predict and plot

## [1] "cannot plot data with prediction"

plotting 2

M261 - Sierran Steppe - Mixed Forest - Coniferous Forest - Alpine Meadow

model selection 1

## Analysis of Variance Table
## 
## Model 1: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
## Model 2: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * (1 - alpha * B_L_prop) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
##   Res.Df Res.Sum Sq Df Sum Sq F value  Pr(>F)  
## 1    160     178.65                            
## 2    159     174.20  1 4.4514  4.0629 0.04552 *
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##   model      AIC
## 1     1 554.3784
## 2     2 552.2656
## 
## Formula: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * (1 - 
##     alpha * B_L_prop) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
## 
## Parameters:
##       Estimate Std. Error t value Pr(>|t|)  
## tau    -1.1541     3.1257  -0.369   0.7125  
## alpha   0.7703     0.3514   2.192   0.0298 *
## A       9.1205     9.6033   0.950   0.3437  
## k     216.5106   149.5716   1.448   0.1497  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.047 on 159 degrees of freedom
## 
## Number of iterations to convergence: 7 
## Achieved convergence tolerance: 1.14e-06
##   (167 observations deleted due to missingness)

summary

  • simple model: fits
  • alpha model: fits

model selection 2

## Error in nls(get(paste("fg_", Mod.Sel1, "a", sep = "")), data = G_M261,  : 
##   object 'ge.fit' not found
## Error in nls(get(paste("fg_", Mod.Sel1, "b", sep = "")), data = G_M261,  : 
##   object 'ge.fit' not found
## Error in nls(get(paste("fg_", Mod.Sel1, "c", sep = "")), data = G_M261,  : 
##   object 'ge.fit' not found
##   model      AIC
## 1     2 552.2656
## 2    2a       NA
## 3    2b       NA
## 4    2c       NA
## 
## Formula: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * (1 - 
##     alpha * B_L_prop) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
## 
## Parameters:
##       Estimate Std. Error t value Pr(>|t|)  
## tau    -1.1541     3.1257  -0.369   0.7125  
## alpha   0.7703     0.3514   2.192   0.0298 *
## A       9.1205     9.6033   0.950   0.3437  
## k     216.5106   149.5716   1.448   0.1497  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.047 on 159 degrees of freedom
## 
## Number of iterations to convergence: 7 
## Achieved convergence tolerance: 1.14e-06
##   (167 observations deleted due to missingness)

summary

  • add p model: does not fit
  • add s model: does not fit
  • add s+p model: does not fit

plot residuals

## 
## ------
##  Shapiro-Wilk normality test
## 
## data:  stdres
## W = 0.9628, p-value = 0.0002337
## 
## 
## ------
## 
##  Runs Test
## 
## data:  as.factor(run)
## Standard Normal = 0.53804, p-value = 0.5906
## alternative hypothesis: two.sided

predict and plot

## Warning: Removed 83 rows containing missing values (`geom_point()`).
## Warning: Removed 1274 rows containing missing values (`geom_line()`).

plotting 2

M262 - Califormia Coastal Range = Coniferous Forest - Open woodland Shrub Meadow

model selection 1

summary

  • simple model: does not fit
  • alpha model: does not fit

model selection 2

summary

  • add p model: does not fit
  • add s model: does not fit
  • add s+p model: does not fit

predict and plot

## [1] "cannot plot data with prediction"

plotting 2

M313 - Arizona-New Mexico Mountains Semi-Desert - Open Woodland - Coniferous Forest - Alpine Meadow

model selection 1

## Error in if (any(nEQ <- vNms != make.names(vNms))) vNms[nEQ] <- paste0("`",  : 
##   missing value where TRUE/FALSE needed
## Error in if (any(nEQ <- vNms != make.names(vNms))) vNms[nEQ] <- paste0("`",  : 
##   missing value where TRUE/FALSE needed
##   model AIC
## 1     1  NA
## 2     2  NA
## Warning in min(AIC1_M313$AIC, na.rm = T): no non-missing arguments to min;
## returning Inf
## Error in h(simpleError(msg, call)) : 
##   error in evaluating the argument 'object' in selecting a method for function 'summary': object 'nls_M313.' not found

summary

  • simple model: does not fit
  • alpha model: does not fit

model selection 2

summary

  • add p model: does not fit
  • add s model: does not fit
  • add s+p model: does not fit

predict and plot

## [1] "cannot plot data with prediction"

plotting 2

M331 - Southern Rocky Mountain Steppe - Open Woodland - Coniferous Forest - Alpine Meadow

model selection 1

## Error in if (any(nEQ <- vNms != make.names(vNms))) vNms[nEQ] <- paste0("`",  : 
##   missing value where TRUE/FALSE needed
## Error in if (any(nEQ <- vNms != make.names(vNms))) vNms[nEQ] <- paste0("`",  : 
##   missing value where TRUE/FALSE needed
##   model AIC
## 1     1  NA
## 2     2  NA
## Warning in min(AIC1_M331$AIC, na.rm = T): no non-missing arguments to min;
## returning Inf
## Error in h(simpleError(msg, call)) : 
##   error in evaluating the argument 'object' in selecting a method for function 'summary': object 'nls_M331.' not found

summary

  • simple model: does not fit
  • alpha model: does not fit

model selection 2

summary

  • add p model: does not fit
  • add s model: does not fit
  • add s+p model: does not fit

predict and plot

## [1] "cannot plot data with prediction"

plotting 2

M332 - Middle Rocky Mountain Steppe - Coniferous Forest - Alpine Meadow

model selection 1

summary

  • simple model: does not fit
  • alpha model: does not fit

model selection 2

summary

  • add p model: does not fit
  • add s model: does not fit
  • add s+p model: does not fit

predict and plot

## [1] "cannot plot data with prediction"

plotting 2

M333 - Northern Rocky Mountain Steppe - Coniferous Forest - Alpine Meadow

model selection 1

summary

  • simple model: does not fit
  • alpha model: does not fit

model selection 2

summary

  • add p model: does not fit
  • add s model: does not fit
  • add s+p model: does not fit

predict and plot

## [1] "cannot plot data with prediction"

plotting 2

M334 - Black Hills Coniferous Forest

model selection 1

## Analysis of Variance Table
## 
## Model 1: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
## Model 2: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * (1 - alpha * B_L_prop) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
##   Res.Df Res.Sum Sq Df Sum Sq F value    Pr(>F)    
## 1    215     222.36                                
## 2    214     201.87  1 20.497  21.729 5.521e-06 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##   model      AIC
## 1     1 662.1249
## 2     2 643.0428
## 
## Formula: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * (1 - 
##     alpha * B_L_prop) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
## 
## Parameters:
##       Estimate Std. Error t value Pr(>|t|)    
## tau    0.09638    1.54157   0.063  0.95021    
## alpha  0.90186    0.16677   5.408  1.7e-07 ***
## A      2.61571    0.91872   2.847  0.00484 ** 
## k     39.67674   14.90211   2.662  0.00835 ** 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.9712 on 214 degrees of freedom
## 
## Number of iterations to convergence: 8 
## Achieved convergence tolerance: 6.091e-06
##   (88 observations deleted due to missingness)

summary

  • simple model: fits
  • alpha model: fits

model selection 2

## Error in nls(get(paste("fg_", Mod.Sel1, "a", sep = "")), data = G_M334,  : 
##   object 'ge.fit' not found
## Error in nls(get(paste("fg_", Mod.Sel1, "b", sep = "")), data = G_M334,  : 
##   object 'ge.fit' not found
## Error in nls(get(paste("fg_", Mod.Sel1, "c", sep = "")), data = G_M334,  : 
##   object 'ge.fit' not found
##   model      AIC
## 1     2 643.0428
## 2    2a       NA
## 3    2b       NA
## 4    2c       NA
## 
## Formula: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * (1 - 
##     alpha * B_L_prop) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
## 
## Parameters:
##       Estimate Std. Error t value Pr(>|t|)    
## tau    0.09638    1.54157   0.063  0.95021    
## alpha  0.90186    0.16677   5.408  1.7e-07 ***
## A      2.61571    0.91872   2.847  0.00484 ** 
## k     39.67674   14.90211   2.662  0.00835 ** 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.9712 on 214 degrees of freedom
## 
## Number of iterations to convergence: 8 
## Achieved convergence tolerance: 6.091e-06
##   (88 observations deleted due to missingness)

summary

  • add p model: does not fit
  • add s model: does not fit
  • add s+p model: does not fit

predict and plot

## Warning: Removed 47 rows containing missing values (`geom_point()`).
## Warning: Removed 1264 rows containing missing values (`geom_line()`).

plotting 2

M341 - Nevada-Utah Mountains Semi-Desert - Coniferous Forest - Alpine Meadow

model selection 1

summary

  • simple model: does not fit
  • alpha model: does not fit

model selection 2

summary

  • add p model: does not fit
  • add s model: does not fit
  • add s+p model: does not fit

predict and plot

## [1] "cannot plot data with prediction"

plotting 2


Fitted parameters

Best / selected models by ecoprovince

Code Ecoregion Sel.Mod
211 Northeastern Mixed Forest 2
212 Laurentian Mixed Forest 2
221 Eastern Broadleaf Forest 2
222 Midwest Broadleaf Forest 2
223 Central Interior Broadleaf Forest 2
231 Southeastern Mixed Forest 2
232 Outer Coastal Plain Mixed Forest 2
234 Lower Mississippi Riverine Forest 2
242 Pacific Lowland Mixed Forest NA
251 Prairie Parkland (Temperate) 2
255 Prairie Parkland (Subtropical) NA
261 California Coastal Chaparral Forest and Shrub NA
262 California Dry Steppe NA
263 California Coastal Steppe - Mixed Forest and Redwood Forest NA
313 Colorado Plateau Semi-Desert NA
315 Southwest Plateau and Plains Dry Steppe and Shrub NA
321 Chihuahuan Semi-Desert NA
322 American Semidesert and Desert NA
331 Great Plains/Palouse Dry Steppe NA
332 Great Plains Steppe 2
341 Intermountain Semi-Desert and Desert NA
342 Intermountain Semi-Desert NA
411 Everglades NA
M211 Adirondack-New England Mixed forest - Coniferous Forest - Alpine Meadow 2
M221 Central Appalachian Broadleaf Forest - Coniferous Forest - Meadow 2
M223 Ozark Broadleaf Forest Meadow 2
M231 Ouachita Mixed Forest 2
M242 Cascade Mixed Forest NA
M261 Sierran Steppe - Mixed Forest - Coniferous Forest - Alpine Meadow 2
M262 California Coastal Range Coniferous Forest - Open Woodland - Shrub - Meadow NA
M313 Arizona-New Mexico Mountains Semi-Desert - Open Woodland - Coniferous Forest - Alpine Meadow NA
M331 Southern Rocky Mountain Steppe - Open Woodland - Coniferous Forest - Alpine Meadow NA
M332 Middle Rocky Mountain Steppe - Coniferous Forest - Alpine Meadow NA
M333 Northern Rocky Mountain Steppe - Coniferous Forest - Alpine Meadow NA
M334 Black Hills Coniferous Forest 2
M341 Nevada-Utah Mountains Semi-Desert - Coniferous Forest - Alpine Meadow NA

table by ecoprovince

Code Ecoregion region n.obs n.plots tau tau.variance tau.2.5 tau.97.5 alpha alpha.variance alpha.2.5 alpha.97.5 A A.2.5 A.97.5 k k.2.5 k.97.5
211 Northeastern Mixed Forest east 4838 2419 0.1164475 0.0282348 -0.2129723 0.4458674 0.6305879 0.0016637 0.5506233 0.7105525 3.597949 3.3484006 3.847497 7.385702 5.8476167 8.923787
212 Laurentian Mixed Forest east 12962 6481 1.2363568 0.0447831 0.8215376 1.6511761 0.8120482 0.0008156 0.7560686 0.8680278 2.543365 2.3634191 2.723312 10.179028 9.0129689 11.345086
221 Eastern Broadleaf Forest east 5446 2723 -0.5929833 0.0206033 -0.8743764 -0.3115902 0.6942633 0.0020776 0.6049058 0.7836208 4.897438 4.5442146 5.250662 14.727831 11.1217236 18.333938
222 Midwest Broadleaf Forest east 3552 1776 0.2070426 0.0739466 -0.3261685 0.7402537 0.8407329 0.0027899 0.7371630 0.9443028 4.207700 3.7306565 4.684743 20.430915 16.4142872 24.447543
223 Central Interior Broadleaf Forest east 6388 3194 -0.8355260 0.0150504 -1.0760297 -0.5950222 0.6417519 0.0025077 0.5435803 0.7399234 5.200768 4.8024420 5.599095 30.290857 24.1075514 36.474162
231 Southeastern Mixed Forest east 7790 3895 1.5672513 0.0444275 1.1540679 1.9804347 0.8719574 0.0007426 0.8185403 0.9253745 3.834827 3.5870605 4.082594 1.357211 0.9247348 1.789687
232 Outer Coastal Plain Mixed Forest east 7940 3970 1.1253118 0.0479741 0.6959541 1.5546695 0.8682073 0.0008366 0.8115100 0.9249047 4.011815 3.7121665 4.311463 5.608382 4.5415246 6.675239
234 Lower Mississippi Riverine Forest east 830 415 0.5317652 0.4742099 -0.8199757 1.8835060 0.7876664 0.0098065 0.5932802 0.9820527 4.119109 3.0208746 5.217344 2.019944 -0.4116854 4.451573
242 Pacific Lowland Mixed Forest west 0 0 NA NA NA NA NA NA NA NA NA NA NA NA NA NA
251 Prairie Parkland (Temperate) east 1392 696 0.0182613 0.2899913 -1.0385063 1.0750289 0.4550613 0.0233662 0.1550890 0.7550335 3.319189 2.5359619 4.102416 10.405567 3.7090586 17.102076
255 Prairie Parkland (Subtropical) east 444 222 NA NA NA NA NA NA NA NA NA NA NA NA NA NA
261 California Coastal Chaparral Forest and Shrub west 0 0 NA NA NA NA NA NA NA NA NA NA NA NA NA NA
262 California Dry Steppe west 0 0 NA NA NA NA NA NA NA NA NA NA NA NA NA NA
263 California Coastal Steppe - Mixed Forest and Redwood Forest west 4 2 NA NA NA NA NA NA NA NA NA NA NA NA NA NA
313 Colorado Plateau Semi-Desert west 0 0 NA NA NA NA NA NA NA NA NA NA NA NA NA NA
315 Southwest Plateau and Plains Dry Steppe and Shrub west 0 0 NA NA NA NA NA NA NA NA NA NA NA NA NA NA
321 Chihuahuan Semi-Desert west 0 0 NA NA NA NA NA NA NA NA NA NA NA NA NA NA
322 American Semidesert and Desert west 0 0 NA NA NA NA NA NA NA NA NA NA NA NA NA NA
331 Great Plains/Palouse Dry Steppe west 118 59 NA NA NA NA NA NA NA NA NA NA NA NA NA NA
332 Great Plains Steppe west 154 77 1.5868390 6.3786373 -3.4080136 6.5816916 0.5937701 NA -0.0069897 1.1945298 2.984765 0.3932120 5.576318 58.321034 10.3693915 106.272676
341 Intermountain Semi-Desert and Desert west 4 2 NA NA NA NA NA NA NA NA NA NA NA NA NA NA
342 Intermountain Semi-Desert west 2 1 NA NA NA NA NA NA NA NA NA NA NA NA NA NA
411 Everglades east 66 33 NA NA NA NA NA NA NA NA NA NA NA NA NA NA
M211 Adirondack-New England Mixed forest - Coniferous Forest - Alpine Meadow east 5108 2554 0.9046325 0.0527318 0.4544509 1.3548140 0.6410909 0.0012094 0.5729141 0.7092676 2.913018 2.6747205 3.151316 2.855283 1.9072259 3.803340
M221 Central Appalachian Broadleaf Forest - Coniferous Forest - Meadow east 5186 2593 0.4574666 0.0500797 0.0187531 0.8961800 0.8004319 0.0048007 0.6646004 0.9362633 3.805030 3.4695975 4.140463 8.319741 4.6485795 11.990902
M223 Ozark Broadleaf Forest Meadow east 602 301 3.0417063 2.7955752 -0.2420279 6.3254406 0.9604446 0.0428776 0.5537696 1.3671195 1.902610 1.0471776 2.758043 9.271931 -2.9695516 21.513413
M231 Ouachita Mixed Forest east 680 340 7.7044298 21.1473763 -1.3250505 16.7339102 0.9156032 0.0243794 0.6090219 1.2221844 1.122126 0.2883033 1.955949 3.448063 -0.5391759 7.435301
M242 Cascade Mixed Forest west 34 17 NA NA NA NA NA NA NA NA NA NA NA NA NA NA
M261 Sierran Steppe - Mixed Forest - Coniferous Forest - Alpine Meadow west 330 165 -1.1540760 9.7697568 -7.3272447 5.0190927 0.7702956 0.1235056 0.0762154 1.4643758 9.120498 -9.8460118 28.087007 216.510553 -78.8927296 511.913836
M262 California Coastal Range Coniferous Forest - Open Woodland - Shrub - Meadow west 8 4 NA NA NA NA NA NA NA NA NA NA NA NA NA NA
M313 Arizona-New Mexico Mountains Semi-Desert - Open Woodland - Coniferous Forest - Alpine Meadow west 0 0 NA NA NA NA NA NA NA NA NA NA NA NA NA NA
M331 Southern Rocky Mountain Steppe - Open Woodland - Coniferous Forest - Alpine Meadow west 0 0 NA NA NA NA NA NA NA NA NA NA NA NA NA NA
M332 Middle Rocky Mountain Steppe - Coniferous Forest - Alpine Meadow west 20 10 NA NA NA NA NA NA NA NA NA NA NA NA NA NA
M333 Northern Rocky Mountain Steppe - Coniferous Forest - Alpine Meadow west 22 11 NA NA NA NA NA NA NA NA NA NA NA NA NA NA
M334 Black Hills Coniferous Forest west 306 153 0.0963763 2.3764227 -2.9422198 3.1349725 0.9018564 0.0278121 0.5731348 1.2305780 2.615705 0.8048144 4.426596 39.676735 10.3030225 69.050449
M341 Nevada-Utah Mountains Semi-Desert - Coniferous Forest - Alpine Meadow west 0 0 NA NA NA NA NA NA NA NA NA NA NA NA NA NA

plot tau

map

## OGR data source with driver: ESRI Shapefile 
## Source: "C:\Users\hogan.jaaron\Dropbox\FIA_R\Mapping\S_USA.EcoMapProvinces\S_USA.EcoMapProvinces.shp", layer: "S_USA.EcoMapProvinces"
## with 37 features
## It has 17 fields
## Integer64 fields read as strings:  PROVINCE_ PROVINCE_I
## Warning: package 'ggnewscale' was built under R version 4.2.1
## Warning: `aes_string()` was deprecated in ggplot2 3.0.0.
## ℹ Please use tidy evaluation ideoms with `aes()`
## Warning: The `size` argument of `element_line()` is deprecated as of ggplot2 3.4.0.
## ℹ Please use the `linewidth` argument instead.
## Warning in grid.Call(C_stringMetric, as.graphicsAnnot(x$label)): font family not
## found in Windows font database

## Warning in grid.Call(C_stringMetric, as.graphicsAnnot(x$label)): font family not
## found in Windows font database
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database

## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database

## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database

## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database

## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database

plot alpha (biomass growth compensation effect)

plot A (asymptote of forest biomass growth in Mg/ha/yr)

## Warning: Removed 20 rows containing missing values (`geom_point()`).

plot k (stand biomass at half biomss G in Mg/ha)

## Warning: Removed 20 rows containing missing values (`geom_point()`).


Analysis 2: No-harvest

211 - Northeastern Mixed Forest

model selection 1

## Analysis of Variance Table
## 
## Model 1: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
## Model 2: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * (1 - alpha * B_L_prop) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
##   Res.Df Res.Sum Sq Df Sum Sq F value    Pr(>F)    
## 1   4721     4916.7                                
## 2   4720     4881.9  1 34.848  33.693 6.876e-09 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##   model      AIC
## 1     1 19060.92
## 2     2 19029.32
## 
## Formula: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * (1 - 
##     alpha * B_L_prop) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
## 
## Parameters:
##       Estimate Std. Error t value Pr(>|t|)    
## tau    0.25718    0.22186   1.159    0.246    
## alpha  0.54983    0.09075   6.059 1.48e-09 ***
## A      3.42289    0.15250  22.446  < 2e-16 ***
## k      5.73464    0.70009   8.191 3.30e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.017 on 4720 degrees of freedom
## 
## Number of iterations to convergence: 7 
## Achieved convergence tolerance: 6.416e-07
##   (48 observations deleted due to missingness)

summary

  • simple model: fits
  • alpha model: fits

model selection 2

## Error in nls(get(paste("fg_", Mod.Sel1, "a", sep = "")), data = G_211,  : 
##   object 'ge.fit' not found
## Error in nls(get(paste("fg_", Mod.Sel1, "b", sep = "")), data = G_211,  : 
##   object 'ge.fit' not found
## Error in nls(get(paste("fg_", Mod.Sel1, "c", sep = "")), data = G_211,  : 
##   object 'ge.fit' not found
##   model      AIC
## 1     2 19029.32
## 2    2a       NA
## 3    2b       NA
## 4    2c       NA
## 
## Formula: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * (1 - 
##     alpha * B_L_prop) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
## 
## Parameters:
##       Estimate Std. Error t value Pr(>|t|)    
## tau    0.25718    0.22186   1.159    0.246    
## alpha  0.54983    0.09075   6.059 1.48e-09 ***
## A      3.42289    0.15250  22.446  < 2e-16 ***
## k      5.73464    0.70009   8.191 3.30e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.017 on 4720 degrees of freedom
## 
## Number of iterations to convergence: 7 
## Achieved convergence tolerance: 6.416e-07
##   (48 observations deleted due to missingness)

summary

  • add p model: does not fit
  • add s model: does not fit
  • add s+p model: does not fit

predict and plot

## Warning: Removed 24 rows containing missing values (`geom_point()`).
## Warning: Removed 1038 rows containing missing values (`geom_line()`).

plotting 2

212 - Laurentian Mixed Forest

model selection 1

## Analysis of Variance Table
## 
## Model 1: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
## Model 2: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * (1 - alpha * B_L_prop) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
##   Res.Df Res.Sum Sq Df Sum Sq F value    Pr(>F)    
## 1  14328      15511                                
## 2  14327      15380  1 131.28  122.29 < 2.2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##   model      AIC
## 1     1 53405.21
## 2     2 53285.40
## 
## Formula: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * (1 - 
##     alpha * B_L_prop) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
## 
## Parameters:
##       Estimate Std. Error t value Pr(>|t|)    
## tau    0.99405    0.18901   5.259 1.47e-07 ***
## alpha  0.54924    0.04753  11.557  < 2e-16 ***
## A      2.61620    0.08343  31.358  < 2e-16 ***
## k     12.92731    0.63594  20.328  < 2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.036 on 14327 degrees of freedom
## 
## Number of iterations to convergence: 6 
## Achieved convergence tolerance: 4.266e-06
##   (2819 observations deleted due to missingness)

summary

  • simple model: fits
  • alpha model: fits

model selection 2

## Error in nls(get(paste("fg_", Mod.Sel1, "a", sep = "")), data = G_212,  : 
##   object 'ge.fit' not found
## Error in nls(get(paste("fg_", Mod.Sel1, "b", sep = "")), data = G_212,  : 
##   object 'ge.fit' not found
## Error in nls(get(paste("fg_", Mod.Sel1, "c", sep = "")), data = G_212,  : 
##   object 'ge.fit' not found
##   model     AIC
## 1     2 53285.4
## 2    2a      NA
## 3    2b      NA
## 4    2c      NA
## 
## Formula: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * (1 - 
##     alpha * B_L_prop) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
## 
## Parameters:
##       Estimate Std. Error t value Pr(>|t|)    
## tau    0.99405    0.18901   5.259 1.47e-07 ***
## alpha  0.54924    0.04753  11.557  < 2e-16 ***
## A      2.61620    0.08343  31.358  < 2e-16 ***
## k     12.92731    0.63594  20.328  < 2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.036 on 14327 degrees of freedom
## 
## Number of iterations to convergence: 6 
## Achieved convergence tolerance: 4.266e-06
##   (2819 observations deleted due to missingness)

summary

  • add p model: does not fit
  • add s model: does not fit
  • add s+p model: does not fit

predict and plot

## Warning: Removed 1414 rows containing missing values (`geom_point()`).
## Warning: Removed 1031 rows containing missing values (`geom_line()`).

plotting 2

221 - Eastern Broadleaf Forest

model selection 1

## Analysis of Variance Table
## 
## Model 1: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
## Model 2: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * (1 - alpha * B_L_prop) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
##   Res.Df Res.Sum Sq Df Sum Sq F value    Pr(>F)    
## 1   5740     7961.8                                
## 2   5739     7865.8  1 96.062  70.088 < 2.2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##   model      AIC
## 1     1 25551.04
## 2     2 25483.33
## 
## Formula: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * (1 - 
##     alpha * B_L_prop) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
## 
## Parameters:
##       Estimate Std. Error t value Pr(>|t|)    
## tau   -0.78625    0.14354  -5.478 4.49e-08 ***
## alpha  0.65120    0.07468   8.720  < 2e-16 ***
## A      5.08801    0.19219  26.474  < 2e-16 ***
## k     14.08252    1.72467   8.165 3.91e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.171 on 5739 degrees of freedom
## 
## Number of iterations to convergence: 5 
## Achieved convergence tolerance: 9.117e-06
##   (59 observations deleted due to missingness)

summary

  • simple model: fits
  • alpha model: fits

model selection 2

## Error in nls(get(paste("fg_", Mod.Sel1, "a", sep = "")), data = G_221,  : 
##   object 'ge.fit' not found
## Error in nls(get(paste("fg_", Mod.Sel1, "b", sep = "")), data = G_221,  : 
##   object 'ge.fit' not found
## Error in nls(get(paste("fg_", Mod.Sel1, "c", sep = "")), data = G_221,  : 
##   object 'ge.fit' not found
##   model      AIC
## 1     2 25483.33
## 2    2a       NA
## 3    2b       NA
## 4    2c       NA
## 
## Formula: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * (1 - 
##     alpha * B_L_prop) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
## 
## Parameters:
##       Estimate Std. Error t value Pr(>|t|)    
## tau   -0.78625    0.14354  -5.478 4.49e-08 ***
## alpha  0.65120    0.07468   8.720  < 2e-16 ***
## A      5.08801    0.19219  26.474  < 2e-16 ***
## k     14.08252    1.72467   8.165 3.91e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.171 on 5739 degrees of freedom
## 
## Number of iterations to convergence: 5 
## Achieved convergence tolerance: 9.117e-06
##   (59 observations deleted due to missingness)

summary

  • add p model: does not fit
  • add s model: does not fit
  • add s+p model: does not fit

predict and plot

## Warning: Removed 27 rows containing missing values (`geom_point()`).
## Warning: Removed 1036 rows containing missing values (`geom_line()`).

plotting 2

222 - Midwest Broadleaf Forest

model selection 1

## Analysis of Variance Table
## 
## Model 1: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
## Model 2: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * (1 - alpha * B_L_prop) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
##   Res.Df Res.Sum Sq Df Sum Sq F value    Pr(>F)    
## 1   3605     4558.0                                
## 2   3604     4485.9  1 72.022  57.862 3.565e-14 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##   model      AIC
## 1     1 14997.96
## 2     2 14942.50
## 
## Formula: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * (1 - 
##     alpha * B_L_prop) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
## 
## Parameters:
##       Estimate Std. Error t value Pr(>|t|)    
## tau   -0.07417    0.25506  -0.291    0.771    
## alpha  0.62537    0.07695   8.126 6.02e-16 ***
## A      4.25283    0.22891  18.579  < 2e-16 ***
## k     18.66572    1.77328  10.526  < 2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.116 on 3604 degrees of freedom
## 
## Number of iterations to convergence: 7 
## Achieved convergence tolerance: 4.332e-06
##   (756 observations deleted due to missingness)

summary

  • simple model: fits
  • alpha model: fits

model selection 2

## Error in nls(get(paste("fg_", Mod.Sel1, "a", sep = "")), data = G_222,  : 
##   object 'ge.fit' not found
## Error in nls(get(paste("fg_", Mod.Sel1, "b", sep = "")), data = G_222,  : 
##   object 'ge.fit' not found
## Error in nls(get(paste("fg_", Mod.Sel1, "c", sep = "")), data = G_222,  : 
##   object 'ge.fit' not found
##   model     AIC
## 1     2 14942.5
## 2    2a      NA
## 3    2b      NA
## 4    2c      NA
## 
## Formula: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * (1 - 
##     alpha * B_L_prop) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
## 
## Parameters:
##       Estimate Std. Error t value Pr(>|t|)    
## tau   -0.07417    0.25506  -0.291    0.771    
## alpha  0.62537    0.07695   8.126 6.02e-16 ***
## A      4.25283    0.22891  18.579  < 2e-16 ***
## k     18.66572    1.77328  10.526  < 2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.116 on 3604 degrees of freedom
## 
## Number of iterations to convergence: 7 
## Achieved convergence tolerance: 4.332e-06
##   (756 observations deleted due to missingness)

summary

  • add p model: does not fit
  • add s model: does not fit
  • add s+p model: does not fit

predict and plot

## Warning: Removed 367 rows containing missing values (`geom_point()`).
## Warning: Removed 1053 rows containing missing values (`geom_line()`).

plotting 2

223 - Central Interior Broadleaf Forest

model selection 1

## Analysis of Variance Table
## 
## Model 1: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
## Model 2: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * (1 - alpha * B_L_prop) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
##   Res.Df Res.Sum Sq Df Sum Sq F value    Pr(>F)    
## 1   6522     8588.1                                
## 2   6521     8541.1  1 47.021    35.9 2.188e-09 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##   model      AIC
## 1     1 27316.33
## 2     2 27282.51
## 
## Formula: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * (1 - 
##     alpha * B_L_prop) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
## 
## Parameters:
##       Estimate Std. Error t value Pr(>|t|)    
## tau   -0.55231    0.15289  -3.613 0.000305 ***
## alpha  0.50136    0.08087   6.200    6e-10 ***
## A      4.12376    0.16155  25.526  < 2e-16 ***
## k     18.46653    2.16245   8.540  < 2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.144 on 6521 degrees of freedom
## 
## Number of iterations to convergence: 18 
## Achieved convergence tolerance: 6.422e-06
##   (906 observations deleted due to missingness)

summary

  • simple model: fits
  • alpha model: fits

model selection 2

## Error in nls(get(paste("fg_", Mod.Sel1, "a", sep = "")), data = G_223,  : 
##   object 'ge.fit' not found
## Error in nls(get(paste("fg_", Mod.Sel1, "b", sep = "")), data = G_223,  : 
##   object 'ge.fit' not found
## Error in nls(get(paste("fg_", Mod.Sel1, "c", sep = "")), data = G_223,  : 
##   object 'ge.fit' not found
##   model      AIC
## 1     2 27282.51
## 2    2a       NA
## 3    2b       NA
## 4    2c       NA
## 
## Formula: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * (1 - 
##     alpha * B_L_prop) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
## 
## Parameters:
##       Estimate Std. Error t value Pr(>|t|)    
## tau   -0.55231    0.15289  -3.613 0.000305 ***
## alpha  0.50136    0.08087   6.200    6e-10 ***
## A      4.12376    0.16155  25.526  < 2e-16 ***
## k     18.46653    2.16245   8.540  < 2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.144 on 6521 degrees of freedom
## 
## Number of iterations to convergence: 18 
## Achieved convergence tolerance: 6.422e-06
##   (906 observations deleted due to missingness)

summary

  • add p model: does not fit
  • add s model: does not fit
  • add s+p model: does not fit

predict and plot

## Warning: Removed 464 rows containing missing values (`geom_point()`).
## Warning: Removed 1146 rows containing missing values (`geom_line()`).

plotting 2

231 - Southeastern Mixed Forest

model selection 1

## Analysis of Variance Table
## 
## Model 1: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
## Model 2: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * (1 - alpha * B_L_prop) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
##   Res.Df Res.Sum Sq Df Sum Sq F value    Pr(>F)    
## 1   9026      16956                                
## 2   9025      16691  1 265.44  143.53 < 2.2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##   model      AIC
## 1     1 44911.25
## 2     2 44770.79
## 
## Formula: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * (1 - 
##     alpha * B_L_prop) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
## 
## Parameters:
##       Estimate Std. Error t value Pr(>|t|)    
## tau    1.83758    0.23485   7.824 5.67e-15 ***
## alpha  0.71230    0.05663  12.579  < 2e-16 ***
## A      3.59730    0.12348  29.133  < 2e-16 ***
## k      0.90233    0.15077   5.985 2.25e-09 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.36 on 9025 degrees of freedom
## 
## Number of iterations to convergence: 8 
## Achieved convergence tolerance: 5.034e-06
##   (238 observations deleted due to missingness)

summary

  • simple model: fits
  • alpha model: fits

model selection 2

## Error in nls(get(paste("fg_", Mod.Sel1, "a", sep = "")), data = G_231,  : 
##   object 'ge.fit' not found
## Error in nls(get(paste("fg_", Mod.Sel1, "b", sep = "")), data = G_231,  : 
##   object 'ge.fit' not found
## Error in nls(get(paste("fg_", Mod.Sel1, "c", sep = "")), data = G_231,  : 
##   object 'ge.fit' not found
##   model      AIC
## 1     2 44770.79
## 2    2a       NA
## 3    2b       NA
## 4    2c       NA
## 
## Formula: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * (1 - 
##     alpha * B_L_prop) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
## 
## Parameters:
##       Estimate Std. Error t value Pr(>|t|)    
## tau    1.83758    0.23485   7.824 5.67e-15 ***
## alpha  0.71230    0.05663  12.579  < 2e-16 ***
## A      3.59730    0.12348  29.133  < 2e-16 ***
## k      0.90233    0.15077   5.985 2.25e-09 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.36 on 9025 degrees of freedom
## 
## Number of iterations to convergence: 8 
## Achieved convergence tolerance: 5.034e-06
##   (238 observations deleted due to missingness)

summary

  • add p model: does not fit
  • add s model: does not fit
  • add s+p model: does not fit

predict and plot

## Warning: Removed 121 rows containing missing values (`geom_point()`).
## Warning: Removed 1017 rows containing missing values (`geom_line()`).

plotting 2

232 - Outer Coastal Plain Mixed Forest

model selection 1

## Analysis of Variance Table
## 
## Model 1: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
## Model 2: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * (1 - alpha * B_L_prop) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
##   Res.Df Res.Sum Sq Df Sum Sq F value    Pr(>F)    
## 1   9259      21370                                
## 2   9258      21035  1 334.48  147.21 < 2.2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##   model      AIC
## 1     1 46753.75
## 2     2 46609.64
## 
## Formula: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * (1 - 
##     alpha * B_L_prop) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
## 
## Parameters:
##       Estimate Std. Error t value Pr(>|t|)    
## tau    1.31980    0.23966   5.507 3.75e-08 ***
## alpha  0.65151    0.05078  12.829  < 2e-16 ***
## A      3.74027    0.14835  25.213  < 2e-16 ***
## k      5.27952    0.48770  10.825  < 2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.507 on 9258 degrees of freedom
## 
## Number of iterations to convergence: 7 
## Achieved convergence tolerance: 9.266e-06
##   (244 observations deleted due to missingness)

summary

  • simple model: fits
  • alpha model: fits

model selection 2

## Error in nls(get(paste("fg_", Mod.Sel1, "a", sep = "")), data = G_232,  : 
##   object 'ge.fit' not found
## Error in nls(get(paste("fg_", Mod.Sel1, "b", sep = "")), data = G_232,  : 
##   object 'ge.fit' not found
## Error in nls(get(paste("fg_", Mod.Sel1, "c", sep = "")), data = G_232,  : 
##   object 'ge.fit' not found
##   model      AIC
## 1     2 46609.64
## 2    2a       NA
## 3    2b       NA
## 4    2c       NA
## 
## Formula: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * (1 - 
##     alpha * B_L_prop) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
## 
## Parameters:
##       Estimate Std. Error t value Pr(>|t|)    
## tau    1.31980    0.23966   5.507 3.75e-08 ***
## alpha  0.65151    0.05078  12.829  < 2e-16 ***
## A      3.74027    0.14835  25.213  < 2e-16 ***
## k      5.27952    0.48770  10.825  < 2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.507 on 9258 degrees of freedom
## 
## Number of iterations to convergence: 7 
## Achieved convergence tolerance: 9.266e-06
##   (244 observations deleted due to missingness)

summary

  • add p model: does not fit
  • add s model: does not fit
  • add s+p model: does not fit

predict and plot

## Warning: Removed 120 rows containing missing values (`geom_point()`).
## Warning: Removed 953 rows containing missing values (`geom_line()`).

plotting 2

234 - Lower Mississippi Riverine Forest

model selection 1

## Analysis of Variance Table
## 
## Model 1: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
## Model 2: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * (1 - alpha * B_L_prop) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
##   Res.Df Res.Sum Sq Df Sum Sq F value    Pr(>F)    
## 1   1035     2282.8                                
## 2   1034     2232.8  1 49.969  23.141 1.729e-06 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##   model      AIC
## 1     1 5204.095
## 2     2 5183.121
## 
## Formula: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * (1 - 
##     alpha * B_L_prop) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
## 
## Parameters:
##       Estimate Std. Error t value Pr(>|t|)    
## tau     0.9863     0.8349   1.181  0.23771    
## alpha   0.7380     0.1432   5.155 3.04e-07 ***
## A       3.8313     0.5718   6.701 3.40e-11 ***
## k       4.5706     1.6297   2.805  0.00513 ** 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.469 on 1034 degrees of freedom
## 
## Number of iterations to convergence: 7 
## Achieved convergence tolerance: 5.062e-06
##   (57 observations deleted due to missingness)

summary

  • simple model: fits
  • alpha model: fits

model selection 2

## Error in nls(get(paste("fg_", Mod.Sel1, "a", sep = "")), data = G_234,  : 
##   object 'ge.fit' not found
## Error in nls(get(paste("fg_", Mod.Sel1, "b", sep = "")), data = G_234,  : 
##   object 'ge.fit' not found
## Error in nls(get(paste("fg_", Mod.Sel1, "c", sep = "")), data = G_234,  : 
##   object 'ge.fit' not found
##   model      AIC
## 1     2 5183.121
## 2    2a       NA
## 3    2b       NA
## 4    2c       NA
## 
## Formula: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * (1 - 
##     alpha * B_L_prop) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
## 
## Parameters:
##       Estimate Std. Error t value Pr(>|t|)    
## tau     0.9863     0.8349   1.181  0.23771    
## alpha   0.7380     0.1432   5.155 3.04e-07 ***
## A       3.8313     0.5718   6.701 3.40e-11 ***
## k       4.5706     1.6297   2.805  0.00513 ** 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.469 on 1034 degrees of freedom
## 
## Number of iterations to convergence: 7 
## Achieved convergence tolerance: 5.062e-06
##   (57 observations deleted due to missingness)

summary

  • add p model: does not fit
  • add s model: does not fit
  • add s+p model: does not fit

predict and plot

## Warning: Removed 31 rows containing missing values (`geom_point()`).
## Warning: Removed 948 rows containing missing values (`geom_line()`).

plotting 2

242 - Pacific Lowland Mixed Forest

model selection 1

## Analysis of Variance Table
## 
## Model 1: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
## Model 2: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * (1 - alpha * B_L_prop) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
##   Res.Df Res.Sum Sq Df Sum Sq F value   Pr(>F)   
## 1     72     125.62                              
## 2     71     111.90  1 13.722  8.7068 0.004292 **
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##   model      AIC
## 1     1 394.7529
## 2     2 388.0772
## 
## Formula: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * (1 - 
##     alpha * B_L_prop) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
## 
## Parameters:
##       Estimate Std. Error t value Pr(>|t|)   
## tau    -1.3783     1.3545  -1.018  0.31236   
## alpha   1.1110     0.3379   3.288  0.00157 **
## A      10.7685     4.3043   2.502  0.01466 * 
## k      20.3645    11.7501   1.733  0.08741 . 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.255 on 71 degrees of freedom
## 
## Number of iterations to convergence: 8 
## Achieved convergence tolerance: 9.971e-06
##   (3 observations deleted due to missingness)

summary

  • simple model: fits
  • alpha model: fits

model selection 2

## Error in nls(get(paste("fg_", Mod.Sel1, "a", sep = "")), data = G_242,  : 
##   object 'ge.fit' not found
## Error in nls(get(paste("fg_", Mod.Sel1, "b", sep = "")), data = G_242,  : 
##   object 'ge.fit' not found
## Error in nls(get(paste("fg_", Mod.Sel1, "c", sep = "")), data = G_242,  : 
##   object 'ge.fit' not found
##   model      AIC
## 1     2 388.0772
## 2    2a       NA
## 3    2b       NA
## 4    2c       NA
## 
## Formula: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * (1 - 
##     alpha * B_L_prop) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
## 
## Parameters:
##       Estimate Std. Error t value Pr(>|t|)   
## tau    -1.3783     1.3545  -1.018  0.31236   
## alpha   1.1110     0.3379   3.288  0.00157 **
## A      10.7685     4.3043   2.502  0.01466 * 
## k      20.3645    11.7501   1.733  0.08741 . 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.255 on 71 degrees of freedom
## 
## Number of iterations to convergence: 8 
## Achieved convergence tolerance: 9.971e-06
##   (3 observations deleted due to missingness)

summary

  • add p model: does not fit
  • add s model: does not fit
  • add s+p model: does not fit

predict and plot

## Warning: Removed 1 rows containing missing values (`geom_point()`).
## Warning: Removed 725 rows containing missing values (`geom_line()`).

plotting 2

251 - Prairie Parkland (Temperate)

model selection 1

## Analysis of Variance Table
## 
## Model 1: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
## Model 2: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * (1 - alpha * B_L_prop) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
##   Res.Df Res.Sum Sq Df Sum Sq F value  Pr(>F)  
## 1   1472     2384.1                            
## 2   1471     2378.2  1 5.9652  3.6897 0.05494 .
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##   model      AIC
## 1     1 6373.760
## 2     2 6372.065
## 
## Formula: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * (1 - 
##     alpha * B_L_prop) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
## 
## Parameters:
##       Estimate Std. Error t value Pr(>|t|)    
## tau     0.5535     0.6090   0.909 0.363576    
## alpha   0.3273     0.1647   1.987 0.047073 *  
## A       2.9823     0.3374   8.838  < 2e-16 ***
## k      12.2340     3.1528   3.880 0.000109 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.271 on 1471 degrees of freedom
## 
## Number of iterations to convergence: 5 
## Achieved convergence tolerance: 6.437e-06
##   (415 observations deleted due to missingness)

summary

  • simple model: fits
  • alpha model: fits

model selection 2

## Error in nls(get(paste("fg_", Mod.Sel1, "a", sep = "")), data = G_251,  : 
##   object 'ge.fit' not found
## Error in nls(get(paste("fg_", Mod.Sel1, "b", sep = "")), data = G_251,  : 
##   object 'ge.fit' not found
## Error in nls(get(paste("fg_", Mod.Sel1, "c", sep = "")), data = G_251,  : 
##   object 'ge.fit' not found
##   model      AIC
## 1     2 6372.065
## 2    2a       NA
## 3    2b       NA
## 4    2c       NA
## 
## Formula: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * (1 - 
##     alpha * B_L_prop) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
## 
## Parameters:
##       Estimate Std. Error t value Pr(>|t|)    
## tau     0.5535     0.6090   0.909 0.363576    
## alpha   0.3273     0.1647   1.987 0.047073 *  
## A       2.9823     0.3374   8.838  < 2e-16 ***
## k      12.2340     3.1528   3.880 0.000109 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.271 on 1471 degrees of freedom
## 
## Number of iterations to convergence: 5 
## Achieved convergence tolerance: 6.437e-06
##   (415 observations deleted due to missingness)

summary

  • add p model: does not fit
  • add s model: does not fit
  • add s+p model: does not fit

predict and plot

## Warning: Removed 201 rows containing missing values (`geom_point()`).
## Warning: Removed 1176 rows containing missing values (`geom_line()`).

plotting 2

255 - Prairie Parkland (Subtropical)

model selection 1

## Error in nls(fg_1, data = G_255, start = c(tau = tau.start, A = A.start,  : 
##   number of iterations exceeded maximum of 50
## Error in nls(fg_2, data = G_255, start = c(tau = tau.start, alpha = alpha.start,  : 
##   number of iterations exceeded maximum of 50
##   model AIC
## 1     1  NA
## 2     2  NA
## Warning in min(AIC1_255$AIC, na.rm = T): no non-missing arguments to min;
## returning Inf
## Error in h(simpleError(msg, call)) : 
##   error in evaluating the argument 'object' in selecting a method for function 'summary': object 'nls_255.' not found

summary

  • simple model: does not fit
  • alpha model: does not fit

model selection 2

summary

  • add p model: does not fit
  • add s model: does not fit
  • add s+p model: does not fit

predict and plot

## [1] "cannot plot data with prediction"

plotting 2

261 - California Coastal Chaparral Forest and Shrub

model selection 1

summary

  • simple model: does not fit
  • alpha model: does not fit

model selection 2

summary

  • add p model: does not fit
  • add s model: does not fit
  • add s+p model: does not fit

predict and plot

## [1] "cannot plot data with prediction"

plotting 2

262 - California Dry Steppe

model selection 1

summary

  • simple model: does not fit
  • alpha model: does not fit

model selection 2

summary

  • add p model: does not fit
  • add s model: does not fit
  • add s+p model: does not fit

predict and plot

## [1] "cannot plot data with prediction"

plotting 2

263 - California Coastal Steppe - Mixed Forest and Redwood Forest

model selection 1

summary

  • simple model: does not fit
  • alpha model: does not fit

model selection 2

summary

  • add p model: does not fit

  • add s model: does not fit

  • add s+p model: does not fit

  • note: model fit, but fit was funky due to data being sparse

predict and plot

## [1] "cannot plot data with prediction"

plotting 2

313 - Colorado Plateau Semi-Desert

model selection 1

## Analysis of Variance Table
## 
## Model 1: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
## Model 2: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * (1 - alpha * B_L_prop) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
##   Res.Df Res.Sum Sq Df Sum Sq F value    Pr(>F)    
## 1    201    104.550                                
## 2    200     96.761  1 7.7888  16.099 8.492e-05 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##   model      AIC
## 1     1 482.3063
## 2     2 468.5127
## 
## Formula: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * (1 - 
##     alpha * B_L_prop) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
## 
## Parameters:
##       Estimate Std. Error t value Pr(>|t|)    
## tau    -1.1219     1.0353  -1.084  0.27983    
## alpha   1.2621     0.2658   4.747 3.92e-06 ***
## A       5.2835     1.9528   2.706  0.00741 ** 
## k     153.4583    53.8954   2.847  0.00487 ** 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.6956 on 200 degrees of freedom
## 
## Number of iterations to convergence: 8 
## Achieved convergence tolerance: 2.523e-06
##   (3 observations deleted due to missingness)

summary

  • simple model: fits
  • alpha model: fits

model selection 2

## Error in nls(get(paste("fg_", Mod.Sel1, "a", sep = "")), data = G_313,  : 
##   object 'ge.fit' not found
## Error in nls(get(paste("fg_", Mod.Sel1, "b", sep = "")), data = G_313,  : 
##   object 'ge.fit' not found
## Error in nls(get(paste("fg_", Mod.Sel1, "c", sep = "")), data = G_313,  : 
##   object 'ge.fit' not found
##   model      AIC
## 1     2 468.5127
## 2    2a       NA
## 3    2b       NA
## 4    2c       NA
## 
## Formula: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * (1 - 
##     alpha * B_L_prop) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
## 
## Parameters:
##       Estimate Std. Error t value Pr(>|t|)    
## tau    -1.1219     1.0353  -1.084  0.27983    
## alpha   1.2621     0.2658   4.747 3.92e-06 ***
## A       5.2835     1.9528   2.706  0.00741 ** 
## k     153.4583    53.8954   2.847  0.00487 ** 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.6956 on 200 degrees of freedom
## 
## Number of iterations to convergence: 8 
## Achieved convergence tolerance: 2.523e-06
##   (3 observations deleted due to missingness)

summary

  • add p model: does not fit
  • add s model: does not fit
  • add s+p model: does not fit

predict and plot

## Warning: Removed 2 rows containing missing values (`geom_point()`).
## Warning: Removed 1103 rows containing missing values (`geom_line()`).

plotting 2

315 - Southwest Plateau and Plains Dry Steppe and Shrub

model selection 1

summary

  • simple model: does not fit
  • alpha model: does not fit

model selection 2

summary

  • add p model: does not fit
  • add s model: does not fit
  • add s+p model: does not fit

predict and plot

## [1] "cannot plot data with prediction"

plotting 2

321 - Chihuahuan Semi-Desert

model selection 1

summary

  • simple model: does not fit
  • alpha model: does not fit

model selection 2

summary

  • add p model: does not fit
  • add s model: does not fit
  • add s+p model: does not fit

predict and plot

## [1] "cannot plot data with prediction"

plotting 2

322 - American Semidesert and Desert

model selection 1

summary

  • simple model: does not fit
  • alpha model: does not fit

model selection 2

summary

  • add p model: does not fit
  • add s model: does not fit
  • add s+p model: does not fit

predict and plot

## [1] "cannot plot data with prediction"

plotting 2

331 - Great Plains/Palouse Dry Steppe

model selection 1

## Error in nls(fg_1, data = G_331, start = c(tau = tau.start, A = A.start,  : 
##   number of iterations exceeded maximum of 50
## Error in nls(fg_2, data = G_331, start = c(tau = tau.start, alpha = alpha.start,  : 
##   number of iterations exceeded maximum of 50
##   model AIC
## 1     1  NA
## 2     2  NA
## Warning in min(AIC1_331$AIC, na.rm = T): no non-missing arguments to min;
## returning Inf
## Error in h(simpleError(msg, call)) : 
##   error in evaluating the argument 'object' in selecting a method for function 'summary': object 'nls_331.' not found

summary

  • simple model: does not fit
  • alpha model: does not fit

model selection 2

summary

  • add p model: does not fit
  • add s model: does not fit
  • add s+p model: does not fit

predict and plot

## [1] "cannot plot data with prediction"

plotting 2

332 - Great Plains Steppe

model selection 1

## Analysis of Variance Table
## 
## Model 1: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
## Model 2: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * (1 - alpha * B_L_prop) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
##   Res.Df Res.Sum Sq Df Sum Sq F value  Pr(>F)  
## 1    182     156.41                            
## 2    181     153.12  1 3.2911  3.8903 0.05009 .
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##   model      AIC
## 1     1 622.4346
## 2     2 620.5005
## 
## Formula: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * (1 - 
##     alpha * B_L_prop) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
## 
## Parameters:
##       Estimate Std. Error t value Pr(>|t|)   
## tau     0.1519     1.3492   0.113  0.91046   
## alpha   0.5797     0.2682   2.161  0.03197 * 
## A       4.2499     1.2855   3.306  0.00114 **
## k      55.1667    18.0272   3.060  0.00255 **
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.9198 on 181 degrees of freedom
## 
## Number of iterations to convergence: 6 
## Achieved convergence tolerance: 2.519e-06
##   (36 observations deleted due to missingness)

summary

  • simple model: fits
  • alpha model: fits

model selection 2

## Error in nls(get(paste("fg_", Mod.Sel1, "a", sep = "")), data = G_332,  : 
##   object 'ge.fit' not found
## Error in nls(get(paste("fg_", Mod.Sel1, "b", sep = "")), data = G_332,  : 
##   object 'ge.fit' not found
## Error in nls(get(paste("fg_", Mod.Sel1, "c", sep = "")), data = G_332,  : 
##   object 'ge.fit' not found
##   model      AIC
## 1     2 620.5005
## 2    2a       NA
## 3    2b       NA
## 4    2c       NA
## 
## Formula: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * (1 - 
##     alpha * B_L_prop) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
## 
## Parameters:
##       Estimate Std. Error t value Pr(>|t|)   
## tau     0.1519     1.3492   0.113  0.91046   
## alpha   0.5797     0.2682   2.161  0.03197 * 
## A       4.2499     1.2855   3.306  0.00114 **
## k      55.1667    18.0272   3.060  0.00255 **
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.9198 on 181 degrees of freedom
## 
## Number of iterations to convergence: 6 
## Achieved convergence tolerance: 2.519e-06
##   (36 observations deleted due to missingness)

summary

  • add p model: does not fit
  • add s model: does not fit
  • add s+p model: does not fit

predict and plot

## Warning: Removed 18 rows containing missing values (`geom_point()`).
## Warning: Removed 1120 rows containing missing values (`geom_line()`).

plotting 2

341 - Intermountain Semi-desert & Desert

model selection 1

summary

  • simple model: does not fit
  • alpha model: does not fit

model selection 2

summary

  • add p model: does not fit
  • add s model: does not fit
  • add s+p model: does not fit

predict and plot

## [1] "cannot plot data with prediction"

plotting 2

342 - Intermountain Semi-Desert

model selection 1

## Analysis of Variance Table
## 
## Model 1: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
## Model 2: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * (1 - alpha * B_L_prop) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
##   Res.Df Res.Sum Sq Df Sum Sq F value  Pr(>F)   
## 1    107     80.765                             
## 2    106     72.925  1 7.8396  11.395 0.00103 **
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##   model      AIC
## 1     1 304.5980
## 2     2 295.3662
## 
## Formula: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * (1 - 
##     alpha * B_L_prop) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
## 
## Parameters:
##       Estimate Std. Error t value Pr(>|t|)    
## tau     2.1411     5.8542   0.366  0.71529    
## alpha   1.0909     0.2798   3.899  0.00017 ***
## A       3.3027     2.8413   1.162  0.24770    
## k      86.3803    35.2633   2.450  0.01594 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.8294 on 106 degrees of freedom
## 
## Number of iterations to convergence: 6 
## Achieved convergence tolerance: 9.792e-06
##   (9 observations deleted due to missingness)

summary

  • simple model: fits
  • alpha model: fits

model selection 2

## Error in nls(get(paste("fg_", Mod.Sel1, "a", sep = "")), data = G_342,  : 
##   object 'ge.fit' not found
## Error in nls(get(paste("fg_", Mod.Sel1, "b", sep = "")), data = G_342,  : 
##   object 'ge.fit' not found
## Error in nls(get(paste("fg_", Mod.Sel1, "c", sep = "")), data = G_342,  : 
##   object 'ge.fit' not found
##   model      AIC
## 1     2 295.3662
## 2    2a       NA
## 3    2b       NA
## 4    2c       NA
## 
## Formula: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * (1 - 
##     alpha * B_L_prop) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
## 
## Parameters:
##       Estimate Std. Error t value Pr(>|t|)    
## tau     2.1411     5.8542   0.366  0.71529    
## alpha   1.0909     0.2798   3.899  0.00017 ***
## A       3.3027     2.8413   1.162  0.24770    
## k      86.3803    35.2633   2.450  0.01594 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.8294 on 106 degrees of freedom
## 
## Number of iterations to convergence: 6 
## Achieved convergence tolerance: 9.792e-06
##   (9 observations deleted due to missingness)

summary

  • add p model: does not fit
  • add s model: does not fit
  • add s+p model: does not fit

predict and plot

## Warning: Removed 7 rows containing missing values (`geom_point()`).
## Warning: Removed 1241 rows containing missing values (`geom_line()`).

plotting 2

411 - Everglades

model selection 1

summary

  • simple model: does not fit
  • alpha model: does not fit

model selection 2

summary

  • add p model: does not fit
  • add s model: does not fit
  • add s+p model: does not fit

predict and plot

## [1] "cannot plot data with prediction"

plotting 2

M211 - Adirondack-New England Mixed forest - Coniferous Forest - Alpine Meadow

model selection 1

## Analysis of Variance Table
## 
## Model 1: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
## Model 2: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * (1 - alpha * B_L_prop) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
##   Res.Df Res.Sum Sq Df Sum Sq F value    Pr(>F)    
## 1   4815     3872.9                                
## 2   4814     3848.1  1  24.79  31.012 2.704e-08 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##   model      AIC
## 1     1 18198.99
## 2     2 18170.05
## 
## Formula: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * (1 - 
##     alpha * B_L_prop) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
## 
## Parameters:
##       Estimate Std. Error t value Pr(>|t|)    
## tau    0.95347    0.26715   3.569 0.000362 ***
## alpha  0.48590    0.08435   5.761 8.90e-09 ***
## A      2.83465    0.13698  20.693  < 2e-16 ***
## k      2.03851    0.40996   4.972 6.84e-07 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.8941 on 4814 degrees of freedom
## 
## Number of iterations to convergence: 7 
## Achieved convergence tolerance: 1.751e-06
##   (20 observations deleted due to missingness)

summary

  • simple model: fits
  • alpha model: fits

model selection 2

## Error in nls(get(paste("fg_", Mod.Sel1, "a", sep = "")), data = G_M211,  : 
##   object 'ge.fit' not found
## Error in nls(get(paste("fg_", Mod.Sel1, "b", sep = "")), data = G_M211,  : 
##   object 'ge.fit' not found
## Error in nls(get(paste("fg_", Mod.Sel1, "c", sep = "")), data = G_M211,  : 
##   object 'ge.fit' not found
##   model      AIC
## 1     2 18170.05
## 2    2a       NA
## 3    2b       NA
## 4    2c       NA
## 
## Formula: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * (1 - 
##     alpha * B_L_prop) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
## 
## Parameters:
##       Estimate Std. Error t value Pr(>|t|)    
## tau    0.95347    0.26715   3.569 0.000362 ***
## alpha  0.48590    0.08435   5.761 8.90e-09 ***
## A      2.83465    0.13698  20.693  < 2e-16 ***
## k      2.03851    0.40996   4.972 6.84e-07 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.8941 on 4814 degrees of freedom
## 
## Number of iterations to convergence: 7 
## Achieved convergence tolerance: 1.751e-06
##   (20 observations deleted due to missingness)

summary

  • add p model: does not fit
  • add s model: does not fit
  • add s+p model: does not fit

predict and plot

## Warning: Removed 14 rows containing missing values (`geom_point()`).
## Warning: Removed 1108 rows containing missing values (`geom_line()`).

plotting 2

M221 - Eastern Broadleaf Forest

model selection 1

## Analysis of Variance Table
## 
## Model 1: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
## Model 2: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * (1 - alpha * B_L_prop) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
##   Res.Df Res.Sum Sq Df Sum Sq F value   Pr(>F)    
## 1   7145      13098                               
## 2   7144      12995  1 103.05  56.652 5.84e-14 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##   model      AIC
## 1     1 33826.78
## 2     2 33772.32
## 
## Formula: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * (1 - 
##     alpha * B_L_prop) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
## 
## Parameters:
##       Estimate Std. Error t value Pr(>|t|)    
## tau    0.52729    0.21178   2.490   0.0128 *  
## alpha  0.70730    0.09049   7.816 6.21e-15 ***
## A      3.65539    0.15155  24.120  < 2e-16 ***
## k      7.61802    1.53132   4.975 6.68e-07 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.349 on 7144 degrees of freedom
## 
## Number of iterations to convergence: 10 
## Achieved convergence tolerance: 6.416e-06
##   (53 observations deleted due to missingness)

summary

  • simple model: fits
  • alpha model: fits

model selection 2

## Error in nls(get(paste("fg_", Mod.Sel1, "a", sep = "")), data = G_M221,  : 
##   object 'ge.fit' not found
## Error in nls(get(paste("fg_", Mod.Sel1, "b", sep = "")), data = G_M221,  : 
##   object 'ge.fit' not found
## Error in nls(get(paste("fg_", Mod.Sel1, "c", sep = "")), data = G_M221,  : 
##   object 'ge.fit' not found
##   model      AIC
## 1     2 33772.32
## 2    2a       NA
## 3    2b       NA
## 4    2c       NA
## 
## Formula: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * (1 - 
##     alpha * B_L_prop) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
## 
## Parameters:
##       Estimate Std. Error t value Pr(>|t|)    
## tau    0.52729    0.21178   2.490   0.0128 *  
## alpha  0.70730    0.09049   7.816 6.21e-15 ***
## A      3.65539    0.15155  24.120  < 2e-16 ***
## k      7.61802    1.53132   4.975 6.68e-07 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.349 on 7144 degrees of freedom
## 
## Number of iterations to convergence: 10 
## Achieved convergence tolerance: 6.416e-06
##   (53 observations deleted due to missingness)

summary

  • add p model: does not fit
  • add s model: does not fit
  • add s+p model: does not fit

predict and plot

## Warning: Removed 28 rows containing missing values (`geom_point()`).
## Warning: Removed 982 rows containing missing values (`geom_line()`).

plotting 2

M223 - Ozark Broadleaf Forest Meadow

model selection 1

## Analysis of Variance Table
## 
## Model 1: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
## Model 2: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * (1 - alpha * B_L_prop) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
##   Res.Df Res.Sum Sq Df Sum Sq F value   Pr(>F)   
## 1    746     1140.6                              
## 2    745     1128.5  1  12.06  7.9616 0.004905 **
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##   model      AIC
## 1     1 3158.420
## 2     2 3152.459
## 
## Formula: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * (1 - 
##     alpha * B_L_prop) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
## 
## Parameters:
##       Estimate Std. Error t value Pr(>|t|)    
## tau     2.5787     1.5196   1.697  0.09013 .  
## alpha   0.8074     0.2698   2.993  0.00286 ** 
## A       1.9230     0.4245   4.530 6.88e-06 ***
## k       7.9486     6.2151   1.279  0.20133    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.231 on 745 degrees of freedom
## 
## Number of iterations to convergence: 19 
## Achieved convergence tolerance: 6.392e-06
##   (6 observations deleted due to missingness)

summary

  • simple model: fits
  • alpha model: fits

model selection 2

## Error in nls(get(paste("fg_", Mod.Sel1, "a", sep = "")), data = G_M223,  : 
##   object 'ge.fit' not found
## Error in nls(get(paste("fg_", Mod.Sel1, "b", sep = "")), data = G_M223,  : 
##   object 'ge.fit' not found
## Error in nls(get(paste("fg_", Mod.Sel1, "c", sep = "")), data = G_M223,  : 
##   object 'ge.fit' not found
##   model      AIC
## 1     2 3152.459
## 2    2a       NA
## 3    2b       NA
## 4    2c       NA
## 
## Formula: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * (1 - 
##     alpha * B_L_prop) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
## 
## Parameters:
##       Estimate Std. Error t value Pr(>|t|)    
## tau     2.5787     1.5196   1.697  0.09013 .  
## alpha   0.8074     0.2698   2.993  0.00286 ** 
## A       1.9230     0.4245   4.530 6.88e-06 ***
## k       7.9486     6.2151   1.279  0.20133    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.231 on 745 degrees of freedom
## 
## Number of iterations to convergence: 19 
## Achieved convergence tolerance: 6.392e-06
##   (6 observations deleted due to missingness)

summary

  • add p model: does not fit
  • add s model: does not fit
  • add s+p model: does not fit

predict and plot

## Warning: Removed 4 rows containing missing values (`geom_point()`).
## Warning: Removed 1175 rows containing missing values (`geom_line()`).

plotting 2

M231 - Ouachita Mixed Forest

model selection 1

## Analysis of Variance Table
## 
## Model 1: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
## Model 2: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * (1 - alpha * B_L_prop) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
##   Res.Df Res.Sum Sq Df Sum Sq F value  Pr(>F)  
## 1    826     1189.6                            
## 2    825     1182.1  1 7.5756  5.2873 0.02173 *
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##   model      AIC
## 1     1 3499.462
## 2     2 3496.166
## 
## Formula: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * (1 - 
##     alpha * B_L_prop) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
## 
## Parameters:
##       Estimate Std. Error t value Pr(>|t|)    
## tau     5.1519     2.9120   1.769 0.077227 .  
## alpha   0.6308     0.2634   2.395 0.016853 *  
## A       1.3964     0.4191   3.332 0.000901 ***
## k       2.2591     1.3416   1.684 0.092579 .  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.197 on 825 degrees of freedom
## 
## Number of iterations to convergence: 15 
## Achieved convergence tolerance: 6.576e-06
##   (11 observations deleted due to missingness)

summary

  • simple model: fits
  • alpha model: fits

model selection 2

## Error in nls(get(paste("fg_", Mod.Sel1, "a", sep = "")), data = G_M231,  : 
##   object 'ge.fit' not found
## Error in nls(get(paste("fg_", Mod.Sel1, "b", sep = "")), data = G_M231,  : 
##   object 'ge.fit' not found
## Error in nls(get(paste("fg_", Mod.Sel1, "c", sep = "")), data = G_M231,  : 
##   object 'ge.fit' not found
##   model      AIC
## 1     2 3496.166
## 2    2a       NA
## 3    2b       NA
## 4    2c       NA
## 
## Formula: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * (1 - 
##     alpha * B_L_prop) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
## 
## Parameters:
##       Estimate Std. Error t value Pr(>|t|)    
## tau     5.1519     2.9120   1.769 0.077227 .  
## alpha   0.6308     0.2634   2.395 0.016853 *  
## A       1.3964     0.4191   3.332 0.000901 ***
## k       2.2591     1.3416   1.684 0.092579 .  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.197 on 825 degrees of freedom
## 
## Number of iterations to convergence: 15 
## Achieved convergence tolerance: 6.576e-06
##   (11 observations deleted due to missingness)

summary

  • add p model: does not fit
  • add s model: does not fit
  • add s+p model: does not fit

predict and plot

## Warning: Removed 1 rows containing missing values (`geom_point()`).
## Warning: Removed 1218 rows containing missing values (`geom_line()`).

plotting 2

M242 - Cascade Mixed Forest

model selection 1

summary

  • simple model: does not fit
  • alpha model: does not fit

model selection 2

summary

  • add p model: does not fit
  • add s model: does not fit
  • add s+p model: does not fit

predict and plot

## [1] "cannot plot data with prediction"

plotting 2

M261 - Sierran Steppe - Mixed Forest - Coniferous Forest - Alpine Meadow

model selection 1

## Analysis of Variance Table
## 
## Model 1: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
## Model 2: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * (1 - alpha * B_L_prop) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
##   Res.Df Res.Sum Sq Df Sum Sq F value    Pr(>F)    
## 1   1603     3802.8                                
## 2   1602     3719.1  1 83.683  36.046 2.379e-09 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##   model      AIC
## 1     1 7718.795
## 2     2 7685.060
## 
## Formula: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * (1 - 
##     alpha * B_L_prop) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
## 
## Parameters:
##       Estimate Std. Error t value Pr(>|t|)    
## tau    -1.8923     0.3279  -5.771 9.43e-09 ***
## alpha   0.7975     0.1230   6.483 1.20e-10 ***
## A      14.1504     1.7044   8.302  < 2e-16 ***
## k     193.9490    25.3297   7.657 3.27e-14 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.524 on 1602 degrees of freedom
## 
## Number of iterations to convergence: 6 
## Achieved convergence tolerance: 2.914e-06
##   (273 observations deleted due to missingness)

summary

  • simple model: fits
  • alpha model: fits

model selection 2

## Error in nls(get(paste("fg_", Mod.Sel1, "a", sep = "")), data = G_M261,  : 
##   object 'ge.fit' not found
## Error in nls(get(paste("fg_", Mod.Sel1, "b", sep = "")), data = G_M261,  : 
##   object 'ge.fit' not found
## Error in nls(get(paste("fg_", Mod.Sel1, "c", sep = "")), data = G_M261,  : 
##   object 'ge.fit' not found
##   model     AIC
## 1     2 7685.06
## 2    2a      NA
## 3    2b      NA
## 4    2c      NA
## 
## Formula: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * (1 - 
##     alpha * B_L_prop) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
## 
## Parameters:
##       Estimate Std. Error t value Pr(>|t|)    
## tau    -1.8923     0.3279  -5.771 9.43e-09 ***
## alpha   0.7975     0.1230   6.483 1.20e-10 ***
## A      14.1504     1.7044   8.302  < 2e-16 ***
## k     193.9490    25.3297   7.657 3.27e-14 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.524 on 1602 degrees of freedom
## 
## Number of iterations to convergence: 6 
## Achieved convergence tolerance: 2.914e-06
##   (273 observations deleted due to missingness)

summary

  • add p model: does not fit
  • add s model: does not fit
  • add s+p model: does not fit

predict and plot

## Warning: Removed 135 rows containing missing values (`geom_point()`).

plotting 2

M262 - Califormia Coastal Range = Coniferous Forest - Open woodland Shrub Meadow

model selection 1

summary

  • simple model: does not fit
  • alpha model: does not fit

model selection 2

summary

  • add p model: does not fit
  • add s model: does not fit
  • add s+p model: does not fit

predict and plot

## [1] "cannot plot data with prediction"

plotting 2

M313 - Arizona-New Mexico Mountains Semi-Desert - Open Woodland - Coniferous Forest - Alpine Meadow

model selection 1

## Analysis of Variance Table
## 
## Model 1: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
## Model 2: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * (1 - alpha * B_L_prop) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
##   Res.Df Res.Sum Sq Df Sum Sq F value    Pr(>F)    
## 1    336     159.99                                
## 2    335     142.37  1 17.619  41.459 4.175e-10 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##   model      AIC
## 1     1 799.7921
## 2     2 762.2385
## 
## Formula: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * (1 - 
##     alpha * B_L_prop) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
## 
## Parameters:
##       Estimate Std. Error t value Pr(>|t|)    
## tau    -2.3762     0.2768  -8.585 3.47e-16 ***
## alpha   0.8155     0.1125   7.250 2.91e-12 ***
## A      10.4659     1.9945   5.247 2.74e-07 ***
## k     164.2182    40.3132   4.074 5.78e-05 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.6519 on 335 degrees of freedom
## 
## Number of iterations to convergence: 7 
## Achieved convergence tolerance: 4.896e-06
##   (1 observation deleted due to missingness)

summary

  • simple model: fits
  • alpha model: fits

model selection 2

## Error in nls(get(paste("fg_", Mod.Sel1, "a", sep = "")), data = G_M313,  : 
##   object 'ge.fit' not found
## Error in nls(get(paste("fg_", Mod.Sel1, "b", sep = "")), data = G_M313,  : 
##   object 'ge.fit' not found
## Error in nls(get(paste("fg_", Mod.Sel1, "c", sep = "")), data = G_M313,  : 
##   object 'ge.fit' not found
##   model      AIC
## 1     2 762.2385
## 2    2a       NA
## 3    2b       NA
## 4    2c       NA
## 
## Formula: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * (1 - 
##     alpha * B_L_prop) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
## 
## Parameters:
##       Estimate Std. Error t value Pr(>|t|)    
## tau    -2.3762     0.2768  -8.585 3.47e-16 ***
## alpha   0.8155     0.1125   7.250 2.91e-12 ***
## A      10.4659     1.9945   5.247 2.74e-07 ***
## k     164.2182    40.3132   4.074 5.78e-05 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.6519 on 335 degrees of freedom
## 
## Number of iterations to convergence: 7 
## Achieved convergence tolerance: 4.896e-06
##   (1 observation deleted due to missingness)

summary

  • add p model: does not fit
  • add s model: does not fit
  • add s+p model: does not fit

predict and plot

## Warning: Removed 1183 rows containing missing values (`geom_line()`).

plotting 2

M331 - Southern Rocky Mountain Steppe - Open Woodland - Coniferous Forest - Alpine Meadow

model selection 1

## Analysis of Variance Table
## 
## Model 1: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
## Model 2: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * (1 - alpha * B_L_prop) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
##   Res.Df Res.Sum Sq Df Sum Sq F value    Pr(>F)    
## 1   1694     1569.2                                
## 2   1693     1477.2  1 92.006  105.45 < 2.2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##   model      AIC
## 1     1 4878.014
## 2     2 4777.476
## 
## Formula: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * (1 - 
##     alpha * B_L_prop) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
## 
## Parameters:
##       Estimate Std. Error t value Pr(>|t|)    
## tau   -0.82833    0.57146  -1.449    0.147    
## alpha  0.70710    0.05849  12.088  < 2e-16 ***
## A      2.51159    0.39307   6.390 2.14e-10 ***
## k     36.74020    6.18642   5.939 3.48e-09 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.9341 on 1693 degrees of freedom
## 
## Number of iterations to convergence: 9 
## Achieved convergence tolerance: 3.496e-06
##   (21 observations deleted due to missingness)

summary

  • simple model: fits
  • alpha model: fits

model selection 2

## Error in nls(get(paste("fg_", Mod.Sel1, "a", sep = "")), data = G_M331,  : 
##   object 'ge.fit' not found
## Error in nls(get(paste("fg_", Mod.Sel1, "b", sep = "")), data = G_M331,  : 
##   object 'ge.fit' not found
## Error in nls(get(paste("fg_", Mod.Sel1, "c", sep = "")), data = G_M331,  : 
##   object 'ge.fit' not found
##   model      AIC
## 1     2 4777.476
## 2    2a       NA
## 3    2b       NA
## 4    2c       NA
## 
## Formula: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * (1 - 
##     alpha * B_L_prop) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
## 
## Parameters:
##       Estimate Std. Error t value Pr(>|t|)    
## tau   -0.82833    0.57146  -1.449    0.147    
## alpha  0.70710    0.05849  12.088  < 2e-16 ***
## A      2.51159    0.39307   6.390 2.14e-10 ***
## k     36.74020    6.18642   5.939 3.48e-09 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.9341 on 1693 degrees of freedom
## 
## Number of iterations to convergence: 9 
## Achieved convergence tolerance: 3.496e-06
##   (21 observations deleted due to missingness)

summary

  • add p model: does not fit
  • add s model: does not fit
  • add s+p model: does not fit

predict and plot

## Warning: Removed 12 rows containing missing values (`geom_point()`).
## Warning: Removed 1091 rows containing missing values (`geom_line()`).

plotting 2

M332 - Middle Rocky Mountain Steppe - Coniferous Forest - Alpine Meadow

model selection 1

summary

  • simple model: does not fit
  • alpha model: does not fit

model selection 2

summary

  • add p model: does not fit
  • add s model: does not fit
  • add s+p model: does not fit

predict and plot

## [1] "cannot plot data with prediction"

plotting 2

M333 - Northern Rocky Mountain Steppe - Coniferous Forest - Alpine Meadow

model selection 1

summary

  • simple model: does not fit
  • alpha model: does not fit

model selection 2

summary

  • add p model: does not fit
  • add s model: does not fit
  • add s+p model: does not fit

predict and plot

## [1] "cannot plot data with prediction"

plotting 2

M334 - Black Hills Coniferous Forest

model selection 1

## Analysis of Variance Table
## 
## Model 1: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
## Model 2: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * (1 - alpha * B_L_prop) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
##   Res.Df Res.Sum Sq Df Sum Sq F value    Pr(>F)    
## 1    242     209.84                                
## 2    241     200.31  1 9.5337   11.47 0.0008256 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##   model      AIC
## 1     1 700.2602
## 2     2 690.8684
## 
## Formula: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * (1 - 
##     alpha * B_L_prop) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
## 
## Parameters:
##       Estimate Std. Error t value Pr(>|t|)    
## tau     1.5146     2.4868   0.609 0.543054    
## alpha   0.8217     0.2258   3.639 0.000335 ***
## A       1.6348     0.6583   2.484 0.013689 *  
## k      27.2124     9.9115   2.746 0.006496 ** 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.9117 on 241 degrees of freedom
## 
## Number of iterations to convergence: 6 
## Achieved convergence tolerance: 6.35e-06
##   (71 observations deleted due to missingness)

summary

  • simple model: fits
  • alpha model: fits

model selection 2

## Error in nls(get(paste("fg_", Mod.Sel1, "a", sep = "")), data = G_M334,  : 
##   object 'ge.fit' not found
## Error in nls(get(paste("fg_", Mod.Sel1, "b", sep = "")), data = G_M334,  : 
##   object 'ge.fit' not found
## Error in nls(get(paste("fg_", Mod.Sel1, "c", sep = "")), data = G_M334,  : 
##   object 'ge.fit' not found
##   model      AIC
## 1     2 690.8684
## 2    2a       NA
## 3    2b       NA
## 4    2c       NA
## 
## Formula: G_MassBal_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * (1 - 
##     alpha * B_L_prop) * A * B_plt_t1_MgHa/(k + B_plt_t1_MgHa)
## 
## Parameters:
##       Estimate Std. Error t value Pr(>|t|)    
## tau     1.5146     2.4868   0.609 0.543054    
## alpha   0.8217     0.2258   3.639 0.000335 ***
## A       1.6348     0.6583   2.484 0.013689 *  
## k      27.2124     9.9115   2.746 0.006496 ** 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.9117 on 241 degrees of freedom
## 
## Number of iterations to convergence: 6 
## Achieved convergence tolerance: 6.35e-06
##   (71 observations deleted due to missingness)

summary

  • add p model: does not fit
  • add s model: does not fit
  • add s+p model: does not fit

predict and plot

## Warning: Removed 36 rows containing missing values (`geom_point()`).
## Warning: Removed 1264 rows containing missing values (`geom_line()`).

plotting 2

M341 - Nevada-Utah Mountains Semi-Desert - Coniferous Forest - Alpine Meadow

model selection 1

summary

  • simple model: does not fit
  • alpha model: does not fit

model selection 2

summary

  • add p model: does not fit
  • add s model: does not fit
  • add s+p model: does not fit

predict and plot

## [1] "cannot plot data with prediction"

plotting 2


Fitted parameters

Best / selected models by ecoprovince

Code Ecoregion Sel.Mod
211 Northeastern Mixed Forest 2
212 Laurentian Mixed Forest 2
221 Eastern Broadleaf Forest 2
222 Midwest Broadleaf Forest 2
223 Central Interior Broadleaf Forest 2
231 Southeastern Mixed Forest 2
232 Outer Coastal Plain Mixed Forest 2
234 Lower Mississippi Riverine Forest 2
242 Pacific Lowland Mixed Forest 2
251 Prairie Parkland (Temperate) 2
255 Prairie Parkland (Subtropical) NA
261 California Coastal Chaparral Forest and Shrub NA
262 California Dry Steppe NA
263 California Coastal Steppe - Mixed Forest and Redwood Forest NA
313 Colorado Plateau Semi-Desert 2
315 Southwest Plateau and Plains Dry Steppe and Shrub NA
321 Chihuahuan Semi-Desert NA
322 American Semidesert and Desert NA
331 Great Plains/Palouse Dry Steppe NA
332 Great Plains Steppe 2
341 Intermountain Semi-Desert and Desert NA
342 Intermountain Semi-Desert 2
411 Everglades NA
M211 Adirondack-New England Mixed forest - Coniferous Forest - Alpine Meadow 2
M221 Central Appalachian Broadleaf Forest - Coniferous Forest - Meadow 2
M223 Ozark Broadleaf Forest Meadow 2
M231 Ouachita Mixed Forest 2
M242 Cascade Mixed Forest NA
M261 Sierran Steppe - Mixed Forest - Coniferous Forest - Alpine Meadow 2
M262 California Coastal Range Coniferous Forest - Open Woodland - Shrub - Meadow NA
M313 Arizona-New Mexico Mountains Semi-Desert - Open Woodland - Coniferous Forest - Alpine Meadow 2
M331 Southern Rocky Mountain Steppe - Open Woodland - Coniferous Forest - Alpine Meadow 2
M332 Middle Rocky Mountain Steppe - Coniferous Forest - Alpine Meadow NA
M333 Northern Rocky Mountain Steppe - Coniferous Forest - Alpine Meadow NA
M334 Black Hills Coniferous Forest 2
M341 Nevada-Utah Mountains Semi-Desert - Coniferous Forest - Alpine Meadow NA

table by ecoprovince

Code Ecoregion region n.obs n.plots tau tau.variance tau.2.5 tau.97.5 alpha alpha.variance alpha.2.5 alpha.97.5 A A.2.5 A.97.5 k k.2.5 k.97.5
211 Northeastern Mixed Forest east 4838 2419 0.2571843 0.0492240 -0.1777745 0.6921431 0.5498301 0.0082358 0.3719155 0.7277448 3.422895 3.1239310 3.721859 5.7346375 4.3621394 7.107136
212 Laurentian Mixed Forest east 12962 6481 0.9940460 0.0357233 0.6235694 1.3645226 0.5492358 0.0022586 0.4560805 0.6423910 2.616201 2.4526674 2.779735 12.9273077 11.6807841 14.173831
221 Eastern Broadleaf Forest east 5446 2723 -0.7862498 0.0206033 -1.0676392 -0.5048605 0.6511983 0.0055768 0.5048017 0.7975948 5.088010 4.7112509 5.464768 14.0825198 10.7015245 17.463515
222 Midwest Broadleaf Forest east 3552 1776 -0.0741662 0.0650581 -0.5742520 0.4259196 0.6253684 0.0059221 0.4744888 0.7762479 4.252835 3.8040332 4.701637 18.6657210 15.1889976 22.142444
223 Central Interior Broadleaf Forest east 6388 3194 -0.5523085 0.0233741 -0.8520149 -0.2526022 0.5013643 0.0065395 0.3428376 0.6598909 4.123765 3.8070702 4.440459 18.4665284 14.2274120 22.705645
231 Southeastern Mixed Forest east 7790 3895 1.8375843 0.0551549 1.3772234 2.2979452 0.7122995 0.0032067 0.6012968 0.8233021 3.597299 3.3552515 3.839347 0.9023328 0.6067866 1.197879
232 Outer Coastal Plain Mixed Forest east 7940 3970 1.3198028 0.0574385 0.8500101 1.7895955 0.6515053 0.0025791 0.5519563 0.7510542 3.740272 3.4494812 4.031062 5.2795174 4.3235172 6.235518
234 Lower Mississippi Riverine Forest east 830 415 0.9863105 0.6969788 -0.6518880 2.6245091 0.7380360 0.0204966 0.4571062 1.0189658 3.831336 2.7093954 4.953276 4.5706428 1.3726943 7.768591
242 Pacific Lowland Mixed Forest west 0 0 -1.3782845 1.8347993 -4.0791758 1.3226068 1.1110433 0.1141936 0.4372390 1.7848476 10.768523 2.1859512 19.351095 20.3645230 -3.0645532 43.793599
251 Prairie Parkland (Temperate) east 1392 696 0.5534955 0.3708856 -0.6411129 1.7481040 0.3272821 NA 0.0042375 0.6503267 2.982338 2.3204311 3.644245 12.2339754 6.0495953 18.418356
255 Prairie Parkland (Subtropical) east 444 222 NA NA NA NA NA NA NA NA NA NA NA NA NA NA
261 California Coastal Chaparral Forest and Shrub west 0 0 NA NA NA NA NA NA NA NA NA NA NA NA NA NA
262 California Dry Steppe west 0 0 NA NA NA NA NA NA NA NA NA NA NA NA NA NA
263 California Coastal Steppe - Mixed Forest and Redwood Forest west 4 2 NA NA NA NA NA NA NA NA NA NA NA NA NA NA
313 Colorado Plateau Semi-Desert west 0 0 -1.1219292 1.0719268 -3.1635102 0.9196518 1.2620845 0.0706721 0.7378711 1.7862979 5.283480 1.4326874 9.134272 153.4582754 47.1820589 259.734492
315 Southwest Plateau and Plains Dry Steppe and Shrub west 0 0 NA NA NA NA NA NA NA NA NA NA NA NA NA NA
321 Chihuahuan Semi-Desert west 0 0 NA NA NA NA NA NA NA NA NA NA NA NA NA NA
322 American Semidesert and Desert west 0 0 NA NA NA NA NA NA NA NA NA NA NA NA NA NA
331 Great Plains/Palouse Dry Steppe west 118 59 NA NA NA NA NA NA NA NA NA NA NA NA NA NA
332 Great Plains Steppe west 154 77 0.1519373 1.8204078 -2.5102953 2.8141698 0.5797203 NA 0.0505029 1.1089378 4.249859 1.7133106 6.786408 55.1666719 19.5962434 90.737100
341 Intermountain Semi-Desert and Desert west 4 2 NA NA NA NA NA NA NA NA NA NA NA NA NA NA
342 Intermountain Semi-Desert west 2 1 2.1411086 34.2718076 -9.4654377 13.7476549 1.0908503 0.0782767 0.5361596 1.6455410 3.302657 -2.3305330 8.935847 86.3802797 16.4674146 156.293145
411 Everglades east 66 33 NA NA NA NA NA NA NA NA NA NA NA NA NA NA
M211 Adirondack-New England Mixed forest - Coniferous Forest - Alpine Meadow east 5108 2554 0.9534702 0.0713666 0.4297432 1.4771972 0.4859013 0.0071145 0.3205422 0.6512604 2.834649 2.5660997 3.103199 2.0385096 1.2347941 2.842225
M221 Central Appalachian Broadleaf Forest - Coniferous Forest - Meadow east 5186 2593 0.5272910 0.0448507 0.1121396 0.9424423 0.7072952 0.0081881 0.5299120 0.8846784 3.655387 3.3582976 3.952476 7.6180239 4.6161892 10.619858
M223 Ozark Broadleaf Forest Meadow east 602 301 2.5786534 2.3092457 -0.4045942 5.5619010 0.8074075 NA 0.2777849 1.3370300 1.922964 1.0895645 2.756363 7.9485577 -4.2526450 20.149760
M231 Ouachita Mixed Forest east 680 340 5.1519387 8.4796824 -0.5638412 10.8677186 0.6307872 0.0693803 0.1137715 1.1478030 1.396358 0.5737630 2.218953 2.2590982 -0.3742218 4.892418
M242 Cascade Mixed Forest west 34 17 NA NA NA NA NA NA NA NA NA NA NA NA NA NA
M261 Sierran Steppe - Mixed Forest - Coniferous Forest - Alpine Meadow west 330 165 -1.8922742 0.1075033 -2.5353869 -1.2491614 0.7975471 0.0151355 0.5562376 1.0388567 14.150408 10.8073327 17.493484 193.9489866 144.2661888 243.631784
M262 California Coastal Range Coniferous Forest - Open Woodland - Shrub - Meadow west 8 4 NA NA NA NA NA NA NA NA NA NA NA NA NA NA
M313 Arizona-New Mexico Mountains Semi-Desert - Open Woodland - Coniferous Forest - Alpine Meadow west 0 0 -2.3762353 0.0766179 -2.9207193 -1.8317513 0.8155232 0.0126547 0.5942410 1.0368054 10.465851 6.5424994 14.389202 164.2181549 84.9193244 243.516985
M331 Southern Rocky Mountain Steppe - Open Woodland - Coniferous Forest - Alpine Meadow west 0 0 -0.8283286 0.3265699 -1.9491767 0.2925194 0.7071035 0.0034215 0.5923755 0.8218314 2.511590 1.7406263 3.282553 36.7401989 24.6063707 48.874027
M332 Middle Rocky Mountain Steppe - Coniferous Forest - Alpine Meadow west 20 10 NA NA NA NA NA NA NA NA NA NA NA NA NA NA
M333 Northern Rocky Mountain Steppe - Coniferous Forest - Alpine Meadow west 22 11 NA NA NA NA NA NA NA NA NA NA NA NA NA NA
M334 Black Hills Coniferous Forest west 306 153 1.5146289 6.1841836 -3.3840132 6.4132710 0.8217487 0.0510030 0.3768795 1.2666179 1.634831 0.3381486 2.931513 27.2123700 7.6882090 46.736531
M341 Nevada-Utah Mountains Semi-Desert - Coniferous Forest - Alpine Meadow west 0 0 NA NA NA NA NA NA NA NA NA NA NA NA NA NA

plot tau

map

## OGR data source with driver: ESRI Shapefile 
## Source: "C:\Users\hogan.jaaron\Dropbox\FIA_R\Mapping\S_USA.EcoMapProvinces\S_USA.EcoMapProvinces.shp", layer: "S_USA.EcoMapProvinces"
## with 37 features
## It has 17 fields
## Integer64 fields read as strings:  PROVINCE_ PROVINCE_I
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database

## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database

## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database

## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database

## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database

## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database

## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database

plot alpha (biomass growth compensation effect)

plot A (asymptote of forest biomass growth in Mg/ha/yr)

## Warning: Removed 15 rows containing missing values (`geom_point()`).

plot k (stand biomass at half biomss G in Mg/ha)

## Warning: Removed 15 rows containing missing values (`geom_point()`).